Reviewerly/exHarmony huggingface dataset:
HiRecS: A Hierarchical Contextual Location Recommendation System

The point-of-interest (POI) recommender exploits check-in information from location-based social networks (LBSNs) to recommend POIs that match user preferences. The preferences of users vary with region/locality, consumption type, and co-consumers. For instance, the places preferred for friends may be different from the places preferred for family, and the places preferred in one locality may be different from the places in another locality. These dynamic preferences can be crucial for an efficient recommender system. A locality consists of different preference trends, such as “known for food and recreational sites.” In real-world, different sets of users might be attracted toward different preference trends, and some users' preferences might overlap across multiple preference sets. Hence, an efficient aggregation of locality preferences is essential for the recommender systems. Many existing studies simply group items by their category and use simple collaborative filtering (CF) for recommendations, however, such techniques cannot efficiently handle the aggregated locality preferences. We propose a hierarchical recommendation model termed Hierarchical Contextual Location Recommendation System (HiRecS) that formulates users' preferences as a hierarchical structure and models the locality trend using aggregated hierarchy. For a locality, the root of hierarchy contains preferred k items from a set of visitors, and the subsequent levels contain preference wise subsets of those items. We also present a hierarchy aggregation technique to aggregate the hierarchical preferences from a similar set of users. The aggregated hierarchy is then contextually exploited for POI sequence recommendation. The core contributions of this article are: 1) it formulates the locality trends as hierarchical structures, presents a hierarchy aggregation technique, and models the personalized POI preferences with the aggregated hierarchy; 2) it exploits the aggregated popular trends to generate contextual POI sequence recommendation; and 3) it extensively evaluates the proposed model with two real-world data sets and demonstrates a significant performance gain of 0.006-5.91 on diversity metrics, 0.0349-17.51 on displacement metrics, and 0.114-0.289 on normalized discounted cumulative gain (NDCG) metrics, when compared to several baselines and relevant studies.
Tip-Tap

We describe Tip-Tap, a wearable input technique that can be implemented without batteries using a custom RFID tag. It recognizes 2-dimensional discrete touch events by sensing the intersection between two arrays of contact points: one array along the index fingertip and the other along the thumb tip. A formative study identifies locations on the index finger that are reachable by different parts of the thumb tip, and the results determine the pattern of contacts points used for the technique. Using a reconfigurable 3x3 evaluation device, a second study shows eyes-free accuracy is 86% after a very short period, and adding bumpy or magnetic passive haptic feedback to contacts is not necessary. Finally, two battery-free prototypes using a new RFID tag design demonstrates how Tip-Tap can be implemented in a glove or tattoo form factor.
A Comprehensive Study of Three Different Portable XRF Scanners to Assess the Soil Geochemistry of An Extensive Sample Dataset

The assessment of soil elemental concentrations nowadays mainly occurs through conventional laboratory analyses. However, proximal soil sensing (PSS) techniques such as X-ray fluorescence (XRF) spectrometry are proving to reduce analysis time and costs, and thus offer a worthy alternative to laboratory analyses. Moreover, XRF scanners are non-destructive and can be directly employed in the field. Although the use of XRF for soil elemental analysis is becoming widely accepted, most previous studies were limited to one scanner, a few samples, a few elements, or a non-diverse sample database. Here, an extensive and diverse soil database was used to compare the performance of three different XRF scanners with results obtained through conventional laboratory analyses. Scanners were used in benchtop mode with built-in soil calibrations to measure the concentrations of 15 elements. Although in many samples Cu, S, P, and Mg concentrations were up to 6, 12, 13, and 5 times overestimated by XRF, and empirical recalibration is recommended, all scanners produced acceptable results, even for lighter elements. Unexpectedly, XRF performance did not seem to depend on soil characteristics such as CaCO3 content. While performances will be worse when expanding to the field, our results show that XRF can easily be applied by non-experts to measure soil elemental concentrations reliably in widely different environments.
Quantum technologies in Russia

Abstract Remarkable advancements in the ability to create, manipulate, and measure quantum systems are paving the way to build next generations of devices based on quantum physics. Quantum technologies in Russia are on the list of strategically important cross-cutting directions in the framework of the National Technology Initiative programs and the Digital Economy National Program. The broad focus includes quantum computing and simulation, quantum communications, quantum metrology and sensing. This paper reviews existing research on quantum science and technologies in Russia and summarizes the main goals for the next few years that form the basis of an upcoming major national initiative.
Imputation-based Ensemble Techniques for Class Imbalance Learning

Correct classification of rare samples is a vital data mining task and of paramount importance in many research domains. This article mainly focuses on the development of the novel class-imbalance learning techniques, which make use of oversampling methods integrated with bagging and boosting ensembles. Two novel oversampling strategies based on the single and the multiple imputation methods are proposed. The proposed techniques aim to create useful synthetic minority class samples, similar to the original minority class samples, by estimation of missing values that are already induced in the minority class samples. The re-balanced datasets are then used to train base-learners of the ensemble algorithms. In addition, the proposed techniques are compared with the commonly used class imbalance learning methods in terms of three performance metrics including AUC, F-measure, and G-mean over several synthetic binary class datasets. The empirical results show that the proposed multiple imputation-based oversampling combined with bagging significantly outperforms other competitors.
On the Relation between Position Information and Sentence Length in Neural Machine Translation

Long sentences have been one of the major challenges in neural machine translation (NMT).Although some approaches such as the attention mechanism have partially remedied the problem, we found that the current standard NMT model, Transformer, has difficulty in translating long sentences compared to the former standard, Recurrent Neural Network (RNN)-based model.One of the key differences of these NMT models is how the model handles position information which is essential to process sequential data.In this study, we focus on the position information type of NMT models, and hypothesize that relative position is better than absolute position.To examine the hypothesis, we propose RNN-Transformer which replaces positional encoding layer of Transformer by RNN, and then compare RNN-based model and four variants of Transformer.Experiments on ASPEC English-to-Japanese and WMT2014 Englishto-German translation tasks demonstrate that relative position helps translating sentences longer than those in the training data.Further experiments on length-controlled training data reveal that absolute position actually causes overfitting to the sentence length.
Automated Classification of Metamodel Repositories: A Machine Learning Approach

Manual classification methods of metamodel repositories require highly trained personnel and the results are usually influenced by the subjectivity of human perception. Therefore, automated metamodel classification is very desirable and stringent. In this work, Machine Learning techniques have been employed for metamodel automated classification. In particular, a tool implementing a feed-forward neural network is introduced to classify metamodels. An experimental evaluation over a dataset of 555 metamodels demonstrates that the technique permits to learn from manually classified data and effectively categorize incoming unlabeled data with a considerably high prediction rate: the best performance comprehends 95.40% as success rate, 0.945 as precision, 0.938 as recall, and 0.942 as F1 score.
Power System Anomaly Detection Based on OCSVM Optimized by Improved Particle Swarm Optimization

This paper tries to solve anomaly detection, a very important issue in ensuring the safe and stable operation of power system. As the proportion of abnormal data in the operation of power system is very small, a one-class support vector machine (OCSVM) is adopted in this paper, which is suitable for classification of unbalanced data. However, the performance of OCSVM is sensitive to its parameters, and an unsuitable choice will decrease the classification accuracy and generalization ability of it. In this paper, particle swarm optimization (PSO) is used to optimize the parameters of OCSVM. The original PSO algorithm converges slowly and easily falls into local optimum. To overcome this issue, this paper proposes an improved PSO algorithm for parameters optimization, in which adaptive speed weighting and adaptive population splitting are introduced to improve the convergence speed of the algorithm and help the algorithm jump out of the local optimal position. Experiments on standard benchmarks and real power system experimental data sets demonstrate the effectiveness of the proposed algorithm.
Real Time Video Object Segmentation in Compressed Domain

Many of the recent methods for semi-supervised video object segmentation are still far from being applicable for real time applications due to their slow inference speed. Therefore, we explore a propagation based segmentation method in compressed domain to accelerate inference speed in this paper. In particular, we only extract the features of I-frames by traditional deep convolutional neural network and produce the features of P-frames through information flow propagation. In the process of feature propagation, we propose two effective components to enhance the representation ability of simply warped features in terms of appearance and location. Specifically, we propose a residual supplement module to supplement appearance information which is lost in direct warping and a spatial attention module that can mine extra spatial saliency to provide the location information of the specified object. Besides, we propose a metric based decoder module which consists of a feature match module and a multi-level refinement module to transform information from semantic representation to shape segmentation mask. Extensive experiments on several video datasets demonstrate that the proposed method can achieve comparable accuracy while much faster inference speed when compared to the state-of-the-art algorithms.
Sine Cosine Algorithm with Multigroup and Multistrategy for Solving CVRP

Sine Cosine Algorithm (SCA) has been proved to be superior to some existing traditional optimization algorithms owing to its unique optimization principle. However, there are still disadvantages such as low solution accuracy and poor global search ability. Aiming at the shortcomings of the sine cosine algorithm, a multigroup multistrategy SCA algorithm (MMSCA) is proposed in this paper. The algorithm executes multiple populations in parallel, and each population executes a different optimization strategy. Information is exchanged among populations through intergenerational communication. Using 19 different types of test functions, the optimization performance of the algorithm is tested. Numerical experimental results show that the performance of the MMSCA algorithm is better than that of the original SCA algorithm, and it also has some advantages over other intelligent algorithms. At last, it is applied to solving the capacitated vehicle routing problem (CVRP) in transportation. The algorithm can get better results, and the practicability and feasibility of the algorithm are also proved.
"Can Secure Behaviors Be Contagious? A Two-Stage Investigation of the Influence of Herd Behavior on Security Decisions "

IT users often make information security-related decisions in complex and multidimensional environments, which could lead to phenomena like behavioral anomalies. For instance, under uncertain circumstances, users may discount their own limited information about a security technology and make their adoption decisions based on what the majority of users’ decisions are in this regard. In this context, imitation can become a legitimate and rational strategy for making security-related decisions. Current behavioral security theories generally assume that users possess sufficient information about security technologies before making security-related decisions. This theory assumption limits our understanding of how security decisions are made in various real-world circumstances. Our research is focused on security behaviors under uncertain circumstances. We investigate how providing popularity information can trigger herd behavior and can subsequently influence security behaviors. We also provide insights into security-related decisions that are influenced by herd mentality and investigate whether they persist over time. Additionally, we conceptualize and operationalize two constructs that can be used in future research to better examine post-adoption security behaviors. The findings of this multistage experiment show that in uncertain circumstances, when users are aware of the widespread use of a certain security technology, they develop a significantly higher intention to engage in protection-motivated behaviors. Furthermore, the results show that at the post-adoption stage, users rely more heavily on their own information about their continuous use of security technologies and put less emphasis on herd-related factors.
Keys for Action: An Efficient Keyframe-Based Approach for 3D Action Recognition Using a Deep Neural Network

In this paper, we propose a novel and efficient framework for 3D action recognition using a deep learning architecture. First, we develop a 3D normalized pose space that consists of only 3D normalized poses, which are generated by discarding translation and orientation information. From these poses, we extract joint features and employ them further in a Deep Neural Network (DNN) in order to learn the action model. The architecture of our DNN consists of two hidden layers with the sigmoid activation function and an output layer with the softmax function. Furthermore, we propose a keyframe extraction methodology through which, from a motion sequence of 3D frames, we efficiently extract the keyframes that contribute substantially to the performance of the action. In this way, we eliminate redundant frames and reduce the length of the motion. More precisely, we ultimately summarize the motion sequence, while preserving the original motion semantics. We only consider the remaining essential informative frames in the process of action recognition, and the proposed pipeline is sufficiently fast and robust as a result. Finally, we evaluate our proposed framework intensively on publicly available benchmark Motion Capture (MoCap) datasets, namely HDM05 and CMU. From our experiments, we reveal that our proposed scheme significantly outperforms other state-of-the-art approaches.
Flexible and Scalable FPGA-Oriented Design of Multipliers for Large Binary Polynomials

With the recent advances in quantum computing, code-based cryptography is foreseen to be one of the few mathematical solutions to design quantum resistant public-key cryptosystems. The binary polynomial multiplication dominates the computational time of the primitives in such cryptosystems, thus the design of efficient multipliers is crucial to optimize the performance of post-quantum public-key cryptographic solutions. This manuscript presents a flexible template architecture for the hardware implementation of large binary polynomial multipliers. The architecture combines the iterative application of the Karatsuba algorithm, to minimize the number of required partial products, with the Comba algorithm, used to optimize the schedule of their computations. In particular, the proposed multiplier architecture supports operands in the order of dozens of thousands of bits, and it offers a wide range of performance-resources trade-offs that is made independent from the size of the input operands. To demonstrate the effectiveness of our solution, we employed the nine configurations of the LEDAcrypt public-key cryptosystem as representative use cases for large-degree binary polynomial multiplications. For each configuration we showed that our template architecture can deliver a performance-optimized multiplier implementation for each FPGA of the Xilinx Artix-7 mid-range family. The experimental validation performed by implementing our multiplier for all the LEDAcrypt configurations on the Artix-7 12 and 200 FPGAs, i.e., the smallest and the largest devices of the Artix-7 family, demonstrated an average performance gain of 3.6x and 33.3x with respect to an optimized software implementation employing the gf2x C library.
Dataset of breast mammography images with masses

Among many cancers, breast cancer is the second most common cause of death in women. Early detection and early treatment reduce breast cancer mortality. Mammography plays an important role in breast cancer screening because it can detect early breast masses or calcification region. One of the drawbacks in breast mammography is breast cancer masses are more difficult to be found in extremely dense breast tissue. We select 106 breast mammography images with masses from INbreast database. Through data augmentation, the number of breast mammography images was increased to 7632. We utilize data augmentation on breast mammography images, and then apply the Convolutional Neural Networks (CNN) models including AlexNet, DenseNet, and ShuffleNet to classify these breast mammography images.
Sentiment Analysis Based Direction Prediction in Bitcoin using Deep Learning Algorithms and Word Embedding Models

Sentiment analysis is a considerable research field to investigate enormous quantity of knowledge and specify user opinions on many subjects and is resumed as the extraction of ideas from the textual data.Like sentiment analysis, Bitcoin which is a digital cryptocurrency also attracts the researchers considerably in the domain of cryptography, computer science, and economics.The objective of this work is to forecast the direction of Bitcoin price by analyzing user opinions in social media such as Twitter.To our knowledge, this is the very first attempt which estimates the direction of Bitcoin price fluctuations by using word embedding models in addition to deep learning techniques in the state-of-the-art studies.For the purpose of estimating the direction of Bitcoin, recurrent neural networks (RNNs), long-short term memory networks (LSTMs), and convolutional neural networks (CNNs) are used as deep learning architectures and Word2Vec, GloVe, and FastText are employed as word embedding models in the experiments.In order to demonstrate the contribution of our work, experiments are carried out on English Twitter dataset.Experiment results show that the usage of FastText model as a word embedding model outperforms other models with 89.13% accuracy value to estimate the direction of Bitcoin price.
PostFinder: Mining Stack Overflow posts to support software developers

Context – During the development of complex software systems, programmers look for external resources to understand better how to use specific APIs and to get advice related to their current tasks. Stack Overflow provides developers with a broader insight into API usage as well as useful code examples. Given the circumstances, tools and techniques for mining Stack Overflow are highly desirable. Objective – In this paper, we introduce PostFinder, an approach that analyzes the project under development to extract suitable context, and allows developers to retrieve messages from Stack Overflow being relevant to the API function calls that have already been invoked. Method – PostFinder augments posts with additional data to make them more exposed to queries. On the client side, it boosts the context code with various factors to construct a query containing information needed for matching against the stored indexes. Multiple facets of the data available are used to optimize the search process, with the ultimate aim of recommending highly relevant SO posts. Results – The approach has been validated utilizing a user study involving a group of 12 developers to evaluate 500 posts for 50 contexts. Experimental results indicate the suitability of PostFinder to recommend relevant Stack Overflow posts and concurrently show that the tool outperforms a well-established baseline. Conclusions – We conclude that PostFinder can be deployed to assist developers in selecting relevant Stack Overflow posts while they are programming as well as to replace the module for searching posts in a code-to-code search engine.
Impact of Innovation on Economic Growth: Evidence from Malaysia

This study empirically investigates the effect of innovation on economic growth&nbsp;using the neoclassical economic growth model. Embarking from the traditional labour&nbsp;growth, physical capital and human capital framework, innovation is postulated to be&nbsp;the main driver for robust economic growth. Using time series techniques, we discover&nbsp;very attention-grabbing findings that highlight the impact of innovation on economic&nbsp;growth for Malaysia. First, the innovation measured by the quantity of a total number&nbsp;of a patent application is statistically insignificant. The result is robust for various&nbsp;innovation measurements, including total local patent application and total foreign&nbsp;patent application. Interestingly, switching to total patent grant instead of a total number&nbsp;of patent application (local or foreign), the empirical result shows a significant impact on&nbsp;economic growth. The finding indirectly reveals the crucial impact of quality innovation&nbsp;rather than the quantity concern. Neglecting both quality and the commercialisation&nbsp;process of these new technologies may not solve the rigidity of knowledge commercialisation&nbsp;paradox. Finally, we test for the prominent institutional quality&nbsp;in mediating economic growth under a knowledge-based economy. The interaction&nbsp;between institutional quality and the total patent grant has significantly accelerated&nbsp;the role of innovation channel to economic growth. The empirical findings imply that&nbsp;inadequacy of innovative technology flow over the long term has a detrimental effect&nbsp;on national innovative capacity. Thus, the innovation-economic growth nexus needs to&nbsp;be complemented with a good institutional quality framework, skilled human capital&nbsp;and broader networking to commercialise the innovative product to ensure that the&nbsp;innovation activities promote economic growth.