Reviewerly/exHarmony huggingface dataset:
A new location‐based privacy protection algorithm with deep learning

Abstract A location‐based recommender system (LbRS) is a system which provides recommendations to a user related to his/her point of interest. In order to generate these recommendations, the LbRS uses personal information regarding the current location of the user. This creates serious privacy issues, as users' movements can be revealed through their location data. This paper proposes a new location‐based privacy protection method, and is divided into two stages. First, dummy locations are identified using query probability and distance. Second, a deep learning algorithm is trained to predict dummy locations. Then, the average entropy of each stage is used to compute final entropy. The results show that the proposed method outperforms standard methods such as random and farthest dummy location selection, although it is fractionally slower than the benchmark methods due to the encryption mechanism integrated into it to provide double‐layer security.
Sentiment Analysis on Tokopedia Product Online Reviews Using Random Forest Method

Tokopedia is one of the most popular e-commerce sites in Indonesia that offers consumer products from various categories. In each product section, a review feature is offered. This review feature became essential in evaluating the sellers and become one consideration for customers in making purchase consideration. Sentiment analysis of Tokopedia product reviews may provide the opportunity to look on how Tokopedia customers respond to product quality and sellers’ hospitality. In evaluating the model, the reviews were grouped as: “positive sentiment” and “negative sentiment” using the Random Forest method and 10-fold cross-validation. Data labelling was carried out automatically by calculating the sentiment score using Lexicon-Based. Visualization of the labelling results was then done using a bar graph and a word cloud on each class of sentiment in order to look up for information that is considered important and most discussed. The test results showed that the accuracy of the Random Forest Method with parameter mtry = 73 and ntree = 50 is 97.38% which leads to the conclusion that the Random Forest Method could well predict the product reviews of Tokopedia. The greater the accuracy, the better performance of the classification model.
Detecting Malicious Users Behind Circuit-Based Anonymity Networks

This project addresses the issue of detecting intruders from hiding behind privacy-protecting anonymity networks. The freely available Tor and the SOCKS proxy services have been popular tools that provide circuit-based anonymous connections to network users. However, recent security breaches reveal that SSH and HTTPS have been used to launch attacks by malicious users by taking advantage of these services to hide their identities. This paper investigates strategies to detect SSH and HTTPS connections via the circuit-based anonymity networks, to help servers and websites defend against anonymous intruders. We evaluate our approaches with SSH and HTTPS connections and show that they achieve high performance for both applications. Our detection algorithms are based on the extra latency delays introduced by the presence of the anonymity networks. Since the latency disparity is sensitive to the location of the anonymity network, our algorithms must be evaluated in the most challenging scenarios. The detection rates for all four combinations of SSH/HTTPS applications via Tor/SOCKS networks were very high, with a low false-positive rate. To demonstrate the robustness of our approach in the Tor case, we tested our method in multiple Tor circuit node selection strategies. The approach can be applied to other applications meeting the same criteria.
This is a BERT. Now there are several of them. Can they generalize to novel words?

Recently, large-scale pre-trained neural network models such as BERT have achieved many state-of-the-art results in natural language processing.Recent work has explored the linguistic capacities of these models.However, no work has focused on the ability of these models to generalize these capacities to novel words.This type of generalization is exhibited by humans (Berko, 1958), and is intimately related to morphology-humans are in many cases able to identify inflections of novel words in the appropriate context.This type of morphological capacity has not been previously tested in BERT models, and is important for morphologically-rich languages, which are under-studied in the literature regarding BERT's linguistic capacities.In this work, we investigate this by considering monolingual and multilingual BERT models' abilities to agree in number with novel plural words in English, French, German, Spanish, and Dutch.We find that many models are not able to reliably determine plurality of novel words, suggesting potential deficiencies in the morphological capacities of BERT models.
Application of Web-based Immersive Virtual Reality in Mathematics Education

This paper deals with design and implementation of web-based, virtual reality (WebVR) application MathworldVR for teaching higher mathematics concepts that require spatial abilities. It describes the use of methods and approaches of instrumental interaction within VR through direct visual manipulation of input variables of parametrized functions, which shortens the time needed for students to understand the underlying principles of a given mathematical theory. Advantages, disadvantages and limitations of developing VR applications for the web in contrast to native desktop applications are discussed. We outline the utilization of A-Frame framework together with the JavaScript library React for fast VR prototyping. Individual components defining the MathworldVR 3D user interface within virtual environment (VE) are presented and we show how various two handed interaction techniques help in the process of teaching mathematics.
Res2U-Net: Image Inpainting via Multi-scale Backbone and Channel Attention

Most Deep learning-based inpainting approaches cannot effectively perceive and present image information at different scales. More often than not, they adopt spatial attention to utilize information on the image background and ignore the effect of channel attention. Hence, they usually produce blurred and poor-quality restored images. In this paper, we propose a novel Res2U-Net backbone architecture to solve these problems. Both encoder and decoder layers of our Res2U-Net employ multi-scale residual structures, which can respectively extract and express multi-scale features of images. Moreover, we modify the network by using the channel attention and introduce a dilated multi-scale channel-attention block that is embedded into the skip-connection layers of our Res2U-Net. This network block can take advantage of low-level features of the encoder layers in our inpainting network. Experiments conducted on the CelebA-HQ and Paris StreetView datasets demonstrate that our Res2U-Net architecture achieves superior performance and outperforms the state-of-the-art inpainting approaches in both qualitative and quantitative aspects.
Distance learning: current state and prospects for development

The article touches upon the priority aspects in education, such as quality, accessibility and cost-effectiveness of learning processes. Existing methods and practices of learning processes call for the need to introduce the innovations in order to solve the urgent problems. One of these innovations was the introduction of modern Internet-based information and communication technologies in the learning process. Modern online technologies were the basic for the new type of learning - distant learning. The brief review of the development of distant learning in the historical aspect proved that this type of learning has become in demand worldwide. Distant learning has its advantages and shortcomings; it cannot replace full-time or part-time education but has become a separate type of learning – a form of self-study using on-line technologies and virtual space, where, in addition to the essential technical aspect, such social factors as student motivation and self-control are important. The status and development prospects of distance learning are analyzed on the basis of two indicators – the volume of the global IP market and the volume of the world distance learning market. Statistical data of both indicators based on reliable sources indicate a constant increase in their values, confirming that distance learning is developing at an increasing pace and therefore is very promising. The aim of developing distance learning in the future is to increase the level of education through the use of modern Internet-based information and communication technologies and a possible concurrent reduction of economic costs.
Graphs from Features: Tree-Based Graph Layout for Feature Analysis

Feature Analysis has become a very critical task in data analysis and visualization. Graph structures are very flexible in terms of representation and may encode important information on features but are challenging in regards to layout being adequate for analysis tasks. In this study, we propose and develop similarity-based graph layouts with the purpose of locating relevant patterns in sets of features, thus supporting feature analysis and selection. We apply a tree layout in the first step of the strategy, to accomplish node placement and overview based on feature similarity. By drawing the remainder of the graph edges on demand, further grouping and relationships among features are revealed. We evaluate those groups and relationships in terms of their effectiveness in exploring feature sets for data analysis. Correlation of features with a target categorical attribute and feature ranking are added to support the task. Multidimensional projections are employed to plot the dataset based on selected attributes to reveal the effectiveness of the feature set. Our results have shown that the tree-graph layout framework allows for a number of observations that are very important in user-centric feature selection, and not easy to observe by any other available tool. They provide a way of finding relevant and irrelevant features, spurious sets of noisy features, groups of similar features, and opposite features, all of which are essential tasks in different scenarios of data analysis. Case studies in application areas centered on documents, images and sound data demonstrate the ability of the framework to quickly reach a satisfactory compact representation from a larger feature set.
Beating the repeaterless bound with adaptive measurement-device-independent quantum key distribution

Surpassing the repeaterless bound is a crucial task on the way towards realizing long-distance quantum key distribution. In this paper, we focus on the protocol proposed by Azuma et al. in [Nature Communications 6, 10171 (2015)], which can beat this bound with idealized devices. We investigate the robustness of this protocol against imperfections in realistic setups, particularly the multiple-photon pair components emitted by practical entanglement sources. In doing so, we derive necessary conditions on the photon-number statistics of the sources in order to beat the repeaterless bound. We show, for instance, that parametric down-conversion sources do not satisfy the required conditions and thus cannot be used to outperform this bound.
Modeling Smart Contracts with Probabilistic Logic Programming

Smart contracts are computer programs that run in a distributed network, the blockchain. These contracts are used to regulate the interaction among parties in a fully decentralized way without the need of a trusted authority and, once deployed, are immutable. The immutability property requires that the programs should be deeply analyzed and tested, in order to ensure that they behave as expected and to avoid bugs and errors. In this paper, we present a method to translate smart contracts into probabilistic logic programs that can be used to analyse expected values of several smart contract’s utility parameters and to get a quantitative idea on how smart contracts variables changes over time. Finally, we applied this method to study three real smart contracts deployed on the Ethereum blockchain.
Block layer decomposition schemes for training deep neural networks

Deep feedforward neural networks’ (DFNNs) weight estimation relies on the solution of a very large nonconvex optimization problem that may have many local (no global) minimizers, saddle points and large plateaus. Furthermore, the time needed to find good solutions of the training problem heavily depends on both the number of samples and the number of weights (variables). In this work, we show how block coordinate descent (BCD) methods can be fruitful applied to DFNN weight optimization problem and embedded in online frameworks possibly avoiding bad stationary points. We first describe a batch BCD method able to effectively tackle difficulties due to the network’s depth; then we further extend the algorithm proposing an online BCD scheme able to scale with respect to both the number of variables and the number of samples. We perform extensive numerical results on standard datasets using various deep networks. We show that the application of BCD methods to the training problem of DFNNs improves over standard batch/online algorithms in the training phase guaranteeing good generalization performance as well.
An Empirical Study on the Cueing Effect of the Emotional Post Title in a Virtual Community

Abstract In a virtual community, the behavior of strengthening the emotion of a post title to draw attention of users is not uncommon, which can affect the overall performance of the information environment. This study focuses on exploring the influence of the emotional information of a post title on the users’ community perceived value in a virtual community. Based on the cue utilization theory, we propose a framework with several hypotheses. Data are collected using the experimental method from the college student sample in our study, and numerous tests are performed to analyze the data and verify the hypotheses. At the end of the study, it is found that the emotional information of the post title reduces the user community perceived financial value and it improves the user community perceived recreational value. The analysis of the mediating role reveals that emotional involvement facilitates the relationship between emotional information of post titles and user community perceived recreational value. This study adds a new dimension by discussing the user community value perception on post title expression and it reveals the conflict of interest between the manager of the virtual community and the producers of the post. Our findings may also provide guidelines and references for virtual community managers. Specifically, they should view the behavior of making post titles more emotional critically, and choose specific information management strategies based on the different value pursuit of community users.
Heterogeneous Network Crawling: Reaching Target Nodes by Motif-Guided Navigation

With numerous nodes on online heterogeneous networks, how to reach and extract target nodes of our specific interests is a pressing problem. In this paper, we propose a novel heterogeneous network crawler, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MCrawl</i> . It addresses the problem via iterative online heterogeneous network crawling by navigating its available APIs, starting from a set of target nodes, i.e., seed nodes. We are facing two challenges towards addressing the problem. First, to navigate within a vast network, how do we start from a small set of target nodes? In other words, which nodes in the “current frontier” and which direction shall we expand, to reach promising target nodes quickly? We propose motif-based crawling to exploit the complex structures and rich semantics of heterogeneous networks. Second, in many scenarios, we do not have a classifier to assess the quality of the harvested nodes and thus the motifs to expand. We develop a probabilistic inference framework to estimate the yield and harvest rates of motifs, achieving principled bootstrapping for crawling. Our experiment on real networks of MCrawl achieves significant margins over baselines.
Detection of Military Targets from Satellite Images using Deep Convolutional Neural Networks

Due to the varying size, orientation, and background of images in the defense sector, it is a daunting task to discern and distinguish the military targets in them. Multitudes of solutions have been proposed in this arena, yet there is a significant need for much better and flawless outputs. In this chapter, we expound on a two-level solution-Edge Boxes and Convolutional Neural Network (CNN) for the detection of targets in satellite imagery, Super resolution of the image using Dense-skip-connections. In the first level, the military objects are detected from the satellite image using Edge Boxes. In satellite imagery, the edge data of targets contains very prominent and concise attributes. The traditionally engineered features such as Histogram of Oriented Gradients, Hough transform and Gabor feature do not work well for huge datasets. However, the Edge Boxes technique generates contours around the target objects and discards the remaining. The output of this level is fed to the second level, wherein, the proposed targets undergo image super resolution. The presented deep learning model tends to inherently learn an end-to-end mapping between images of lower resolution and higher resolution. This level can be portrayed as one which takes a low-resolution input image and constructs an up-sampled high-resolution image as the output. Unlike traditional methods (sparse coding based method, bicubic method) that handle each component separately, this method aims to optimize all the layers at once. Furthermore, for assuaging the vanishing gradient problem that is common to very deep networks, Dense-skip-connections are employed. These enable the building of shorter paths directly within multiple layers. Though the proposed model has a light weighted structure, it exhibits state-of-the-art restoration quality.
Hand Movement Identification Using Single-Stream Spatial Convolutional Neural Networks

Human-robot interaction can be through several ways, such as through device control, sounds, brain, and body, or hand gesture. There are two main issues: the ability to adapt to extreme settings and the number of frames processed concerning memory capabilities. Although it is necessary to be careful with the selection of the number of frames so as not to burden the memory, this paper proposed identifying hand gesture of video using Spatial Convolutional Neural Networks (CNN). The sequential image's spatial arrangement is extracted from the frames contained in the video so that each frame can be identified as part of one of the hand movements. The research used VGG16, as CNN architecture is concerned with the depth of learning where there are 13 layers of convolution and three layers of identification. Hand gestures can only be identified into four movements, namely `right', `left', `grab', and `phone'. Hand gesture identification on the video using Spatial CNN with an initial accuracy of 87.97%, then the second training increased to 98.05%. Accuracy was obtained after training using 5600 training data and 1120 test data, and the improvement occurred after manual noise reduction was performed.
Rule-Based Path Planning for Unmanned Aerial Vehicles in Non-Segregated Air Space over Congested Areas

In this paper, we present an approach for simplified path planning for unmanned aerial vehicles (UAVs) in obstacle-dense high-risk areas. We reduce the complexity of the 3D planning problem to that of a 2D planning problem by leveraging regulatory restrictions and guidelines as well as mission-specific boundary conditions to simplify the configuration space. This is achieved through strict limitations and a projection of the search space into a quasi-2D plane. We further suggest a modular motion planning architecture of multiple planners, each of which is taylored to a specific flight phase and displays deterministic behaviour in memory and runtime complexity. Through the integration of regulatory considerations, we seek to provide a planning result that complies with future regulations for flights over congested areas beyond the visual line of sight (BVLOS) and allows feasible solutions for dense multi-vehicle operation of urban routes. In addition to the methodological approach, we introduce the application of image processing techniques to the generation of roadmaps from available maps. We apply the method to a mission scenario of realistic extend and show how it scales to obstacle-dense environments. The results indicate that efficient pre-processing of environment data can enable regulation-compliant path-planning for UAVs in urban environments on consumer hardware.
Virtual Machine Performance Analysis and Prediction

Virtualization technology promotes the development of cloud computing technology, and cloud service providers provide many virtual machines with different configurations. It is becoming more and more important to analyze the performance of virtual machines based on the characteristics of the workload. This paper analyzes the performance of memory-intensive, compute-intensive and I/O intensive workloads running in virtual machines with different sizes. In order to avoid excessive overhead when comparing the performance of virtual machines, this paper proposes a performance prediction method for virtual machines based on the results of micro-benchmarks. Finally, this paper proposes the concept of virtual machine consistency. If a workload has the same performance in two virtual machines, we call the performance of the two virtual machines consistent with respect to this workload.
Understanding the computational difficulty of a binary-weight perceptron and the advantage of input sparseness

Limited precision of synaptic weights is a key aspect of both biological and hardware implementation of neural networks. To assign low-precise weights during learning is a non-trivial task, but may benefit from representing to-be-learned items using sparse code. However, the computational difficulty resulting from low weight precision and the advantage of sparse coding remain not fully understood. Here, we study a perceptron model, which associates binary (0 or 1) input patterns with outputs using binary (0 or 1) weights, modeling a single neuron receiving excitatory inputs. We considered a decimation process, where every time step, marginal probabilities of unfixed weights were evaluated, then the most polarized weight was fixed at its preferred value. We showed that decimation is a process approaching the dense solution region in weight configuration space. In two efficient algorithms (SBPI and rBP) for solving binary-weight perceptron, most time steps are spent on determining values of the weights fixed late in decimation. This algorithmic difficult point may result from strong cross-correlation between late-decimation-fixed weights in the solution subspace where early-decimation-fixed weights take their fixed values, and is related to solution condensation in this subspace during decimation. Input sparseness reduces the time steps that SBPI and rBP need to find solutions, by reducing time steps used to assign values to late-decimation-fixed weights, due to the reduction of cross-correlation between late-decimation-fixed weights. Our work suggests that the computational difficulty of constraint satisfaction problem originates from the subspace of late-decimation-fixed variables. Our work highlights the heterogeneity of learning dynamics of weights, which may help understand axonal pruning in brain development, and inspire more efficient algorithms to train artificial neural networks.