Reviewerly/exHarmony huggingface dataset:
An Empirical Investigation of Students’ Attitudes Towards the Use of Social Media in Omani Higher Education

Although social media usage in higher education has been frequently examined, little is known regarding the differences in students’ attitudes towards its use in the Omani higher education. Therefore, this study provides an opportunity to advance the understanding of these differences in attitudes by focusing on three different attributes, namely age, gender, and social media application. A total of 169 students enrolled at Sultan Qaboos University in Muscat, Oman took part in the study by the medium of an online survey. The empirical data analysis triggered out that there was a significant difference in attitudes with regard to age groups, in which students aged between 18 and 22 years old are much interested in using social media than the others. Nevertheless, the findings also showed that there was no significant difference in attitudes with respect to social media application and gender. More interesting, the outcomes pointed out that WhatsApp is the most frequent application used by students for educational purposes.
Effects of hydrogen-like impurity on the coherence time of asymmetric Gaussian confinement potential quantum well qubit

We calculate the coherent time of a qubit formed between the ground state (GS) and the first excited state (FES) of strong-coupling polaron in an asymmetric Gaussian confinement potential quantum well (AGCPQW) with a hydrogen-like impurity at the coordinate origin. The coherent time changing with the strength of the Coulombic impurity potential (CIP), the AGCPQW's height, the range of the limited potential (RLP), the oscillating frequency of the AGCPQW and the radius of the polaron are presented. Our numerical results indicate that the coherent time of the qubit depends on the strength of the CIP, the height of the AGCPQW, the radius of the polaron and the RLP. These physical parameters are important for adjusting the coherence time to realize stable qubit.
Corrections to anisotropic diffuse radiation model

Solar diffuse radiation models are based on isotropic and on anisotropic models. The anisotropic models take also into account the circumsolar and horizon brightenings. Both types of models were developed for a single row. A photovoltaic field is deployed in multiple rows, therefore the second (and subsequent rows) “sees” the sky dome with a smaller angle than the first row. Obscuring part of the sky affects the diffuse incident radiation. The present article modifies the anisotropic model by three factors: (a) Sky view factor of a second row (b) Correction to circumsolar brightening, (c) Correction to horizon brightening. The modified model is demonstrated on Klucher model for diffuse radiation. The study compares the differences in the diffuse incident irradiation on rows for isotropic, anisotropic and modified anisotropic Klucher models for the first and the second row. For latitude 32°N and relative low percentage of diffuse radiation, the horizon brightening may not be taken into account in the diffuse radiation calculations as it constitutes only about 0.1%. The isotropic model resulted in 9.56% less annual diffuse incident irradiation as compared to Klucher original model for the front row. The isotropic model predicts 5.83% less annual diffuse incident irradiation compared to the modified Klucher model for the second and subsequent rows. The proposed modified anisotropic model enables to calculate the incident solar radiation on photovoltaic fields more accurately.
High-Precision Calibration of Omnidirectional Camera Using an Iterative Method

A distorted checkerboard image affects the precision of omnidirectional camera calibration because of the inaccurate localization of the feature points. To solve this problem, an iterative refinement method is presented. First, the initial parameters are obtained using the traditional calibration method, and the distorted checkerboard is transformed to a distortion-free plane by the projection model that is estimated by initial parameters. Then, the feature point coordinates of the corrected image are extracted. The calibration parameters are recomputed using the refinement of new point locations until the camera parameters converge. This iterative refinement method improves the localization accuracy of feature points and, consequently, of camera calibration. The correctness and effectiveness of the method are verified by a series of simulations and physical experiments. The experiments show that the reprojection errors of feature points are reduced by 39% compared to traditional methods.
ClustCrypt: Privacy-Preserving Clustering of Unstructured Big Data in the Cloud

Security and confidentiality of big data stored in the cloud are important concerns for many organizations to adopt cloud services. One common approach to address the concerns is client-side encryption where data is encrypted on the client machine before being stored in the cloud. Having encrypted data in the cloud, however, limits the ability of data clustering, which is a crucial part of many data analytics applications, such as search systems. To overcome the limitation, in this paper, we present an approach named ClustCrypt for efficient topic-based clustering of encrypted unstructured big data in the cloud. ClustCrypt dynamically estimates the optimal number of clusters based on the statistical characteristics of encrypted data. It also provides clustering approach for encrypted data. We deploy ClustCrypt within the context of a secure cloud-based semantic search system (S3BD). Experimental results obtained from evaluating ClustCrypt on three datasets demonstrate on average 60% improvement on clusters' coherency. ClustCrypt also decreases the search-time overhead by up to 78% and increases the accuracy of search results by up to 35%.
Challenges and Recommended Solutions in Multi-Source and Multi-Domain Sentiment Analysis

The massive availability of online reviews and postings in social media offers invaluable feedback for businesses to make better informed decisions in steering their marketing strategies towards users' interests and preferences.Sentiment analysis is, therefore, essential for determining the public's opinion towards a particular topic, product or service.Traditionally, sentiment analysis is performed on a single data source, for instance, online product reviews or Tweets.However, the need to develop a more precise, and more comprehensive result has steered the move towards performing sentiment analysis on multiple data sources.The use of multiple data sources for a particular domain of interest can increase the amount of datasets needed for training a sentiment classifier.Till now, the problem of insufficient datasets for training the classifier is only addressed by multi-domain sentiment analysis.Aiming to equip researchers with a thorough understanding on both multi-source and multi-domain sentiment analysis, this paper aims to identify the underlying challenges of multi-source and multi-domain sentiment analysis, and discuss the solutions applied by the researchers concerned.This paper also offers an insightful discussion of the findings derived from past studies, and based on these, propose some useful suggestions for the future direction of this research area.Findings derived from our review would be beneficial towards guiding researchers towards the future progress and advancement of multi-source and multi-domain sentiment analysis.
Cascaded Boundary Network for High-Quality Temporal Action Proposal Generation

Creating high-quality temporal action proposals is fundamental yet challenging for accurate action detection in untrimmed videos due to the complexity of the background and variation in actions' durations and magnitudes. In this paper, we propose a cascaded boundary network (CBN) to predict the action boundaries by considering the importance of precise boundary information to develop accurate action proposals. Specifically, the first stage of CBN locates the temporal boundaries by predicting the probability that each frame corresponds to an action, the start position, and the end position. A temporal convolutional network is used in this stage to capture short-term context information. Next, the predicted probabilities are forwarded to the second stage, in which a long short-term memory (LSTM) network is utilized for further refinement by exploiting the correlation between the predicted probabilities to capture long-term context information. Finally, we combine the results from both stages to produce a long- and short-term information fusion. The experiments on THUMOS14 and ActivityNet-1.3 show that CBN achieves state-of-the-art recall performance. The performance improvement is especially remarkable for a small average number (AN) of retrieved proposals; e.g., the average recall at AN=50 on THUMOS14 is improved from 37.46% to 43.06%. Further experiments are performed by introducing proposals generated by CBN into an existing action detection framework. CBN also achieves state-of-the-art average mAP@tIoU on the THUMOS14 detection benchmark.
A Contrastive Study of Chinese Text Segmentation Tools in Marketing Notification Texts

Abstract It is necessary to analyze and mining marketing notification texts because there are various commercial information. The base of the operation is Chinese word segmentation. The speed and accuracy of word segmentation have important influence on the subsequent texts mining. We compared accuracy, recall, and F-value of four open-source Chinese word segmentation tools (Ansj, HanLP, Word and Jieba) on the third-party datasets. Then, we compared the segmentation speed of the four tools on one million marketing notification texts. Finally, we segmented 5, 000 marketing notification texts artificially. We evaluated the performance of these segmentation tools by the results of artificial segmentation, which are known as evaluate standard. The experiments show the Base mode of the Ansj is the fastest. The HanLP is a best segmentation tool for balancing speed and accuracy of word segmentation. After adding a custom dictionary, the effect of word segmentation has been significantly improved.
Elderly Users' Interaction with Conversational Agent

Exploring how users behave when they interact with a conversational agent has been a popular research topic recently. However, very few studies have focused on the unique features of old people's interaction with an agent. In this paper, we report the results of interviews conducted with 19 participants, with ages comprised between 30 and 70 years. The interviews were conducted after the participants used the conversational agent, "Clova" for two weeks. During the interview, the subjects were asked about the frequently used functions and the satisfying and unsatisfying aspects of the agent. In this study, the participants with ages of 50 years or older were classified as older adults, while those under the age of 50 were classified as younger adults. Then we compared the characteristics of these two user groups by conducting a text analysis of the interview script. Our finding indicated that, older adults tended to personify the agent more by using polite words such as 'Grateful', while younger adults tended to consider it as a tool by placing more importance on its convenience; Also, older adults perceived the music function as having a high importance compared to the younger adults.
Parallel convolution algorithm using implicit matrix multiplication on multi-core CPUs

Convolution neural networks (CNNs) have been extensively used in machine learning applications. The most time-consuming part of CNNs are convolution operations. A common approach to implementing convolution operations is to recast them as general matrix multiplication, known as the im2col+GEMM approach. There are two main drawbacks of this approach. One is that large additional memory space is required. The other is the packing on the input elements of convolution operations are not memory-efficient enough. In this paper, we present a new parallel convolution algorithm using implicit matrix multiplication on multi-core CPUs. In comparison with Im2col+GEMM, our new algorithm can reduce the memory footprints and improve the packing efficiency. The experiment results on two ARV8-based multi-core CPUs demonstrate that our new algorithm gives much better performance and scalability than the im2col+GEMM method in most cases.
Automatic prior shape selection for image edge detection with modified Mumford–Shah model

Edge detection plays an important role in the field of image processing. In this paper, we propose a novel variational model to automatically and adaptively detect one or more prior shapes from the given dictionary to guide the edge detection process. In that way, we can effectively detect the shapes of interest from the test image. Moreover, an efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) is proposed to solve this model with guaranteed convergence. A variety of numerical experiments show that the proposed method has achieved ideal performance for edge detection in images with missing information, various types of noise and complicated background, and even multiple objects.
GRU-ES: Resource Usage Prediction of Cloud Workloads Using a Novel Hybrid Method

Resource usage prediction is to predict future resource usage by analyzing past resource usage, so it is essentially a time series analysis problem. Cloud computing enables shared hardware and software resources to be provided to various terminal devices on demand. Because cloud resources are always in the process of dynamic change, it is difficult for prediction algorithms to accurately predict resource usage. Therefore, we propose a novel hybrid method to reduce the error rate of resource usage prediction, named GRU-ES, which combines the gated recurrent unit model and the exponential smoothing method. The method obtains the intermediate results predicted by the gated recurrent unit model. The intermediate results are processed by the exponential smoothing and the predicted results are finally obtained. In addition, in order to solve the short-term unpredictability of resource usage, we propose a multi-step prediction scheme to predict the long-term future resource usage. Experimental results demonstrate that GRU-ES can obtain higher accuracy of resource usage prediction than state-of-the-art models in both single-step prediction and multi-step prediction.
Detection of Tor Traffic Hiding Under Obfs4 Protocol Based on Two-Level Filtering

Tor (The second generation Onion Router) is the most popular anonymous communication network. In order to protect Tor user from traffic analysis attack, many obfuscation techniques are adopted and Obfs4 is one of the states of art techniques used in Tor. It is very hard to detect the Tor traffic camouflaged under Obfs4, especially in the real world when there is a large volume of various traffic, because of random padding and randomization of time sequence. In this paper, we propose a novel scheme for Obfs4 traffic detection based on two-level filtering. We sequentially utilize coarse-grained fast filtering and fine-grained accurate identification to achieve high-precision, real-time recognition of Obfs4 traffic. In the coarse-grained filtering phase, we use the randomness detection algorithm to detect the randomness of the handshake packet payload in the communication and use the timing sequence characteristics of the packet in the handshake process to remove other interference traffic. In the fine-grained identification phase, we analyze its statistical feature on a large number of Obfs4 traffic and use the classification algorithms to identify the Obfs4 traffic. We train and test with different classifiers. The experiments show that the accuracy for identifying Obfs4 is above 99% when using the SVM (Support Vector Machine) algorithm, which indicates that Obfs4 cannot effectively counteract traffic analysis attacks in practical applications.
Scene Recognition via Object-to-Scene Class Conversion: End-to-End Training

When a person recognize the scene of an image, contextual understanding from its environmental elements is necessary. These environmental elements are variant and require comprehensive understanding of various situations. Especially, objects are frequently used as environmental elements related with scene. In this paper, we suggest a score level Class Conversion Matrix (CCM) for scene recognition with a great focus on relationship between objects and scene. A lot of existing methods have already build scene recognition systems with consideration of close relationship between object and scenes. However, most of these methods are using the object features directly without any conversions or reconstructions, and it lack confirmation whether these object features are helpful to recognize scenes correctly. To solve this problem, CCM, a matrix converting object feature to scene feature, is suggested. Moreover, CCM can be implemented with neural network layer and end-to-end trainable. Extensive experiments on Places 2 dataset demonstrate the effectiveness of our approach, when it is applied to the existing deep convolutional neural network architectures. The code is available at https://github.com/Hongje/Class_Conversion_Matrix-Places365
A Music Recommendation System Based on logistic regression and eXtreme Gradient Boosting

With the rapid growth of music industry data, it is difficult for people to find their favorite songs in the music library. Therefore, people urgently need an efficient music recommendation system to help them retrieve music. Traditional collaborative filtering algorithms are applied to the field of music recommendation. However, collaborative filtering does not handle data sparse problems very well when new items are introduced. To solve this problem, some people use the logistic regression method as a classifier to predict the user's music preferences to recommend songs. Logistic regression is a linear model that does not handle complex non-linear data features. In this paper, we propose a hybrid LX recommendation algorithm by integrating logistic regression and eXtreme Gradient Boosting(xgboost). A series of experiments are conducted on a real music dataset to evaluate the effectiveness of our proposed LX model. Our results show that the error and AUC of our LX model are better than other methods.
Two-stage Unsupervised Video Anomaly Detection using Low-rank based Unsupervised One-class Learning with Ridge Regression

Video anomaly detection is a valuable but challenging task, especially in the field of surveillance videos for public safety. Almost all existing methods tackle the problem under the supervised setting and only a few attempts are conducted on the unsupervised learning. To avoid the cost of labeling training videos, this paper proposes to discriminate anomaly by a novel two-stage framework in a fully unsupervised manner. Unlike previous unsupervised approaches using local change detection to discover abnormality, our method enjoys the global information from video context by considering the pair-wise similarity of all video events. In this way, our method formulates video anomaly detection as an extension of unsupervised one-class learning, which has not been explored in the literature of video anomaly detection. Specifically, our method consists of two stages: The first stage of our kernel-based method, named Low-rank based Unsupervised One-class Learning with Ridge Regression (LR-UOCL-RR), reformulates the optimization goal of UOCL with ridge regression to avoid expensive computation, which enables our method to handle massive unlabeled data from videos. In the second stage, the estimated normal video events from the first stage are fed into the one-class support vector machine to refine the profile around normal events and enhance the performance. The experimental results conducted on two challenging video benchmarks indicate that our method is considerably superior, up to 15:7% AUC gain, to the state-of-the-art methods in the unsupervised anomaly detection task and even better than several supervised approaches.
Puncturable Signatures and Applications in Proof-of-Stake Blockchain Protocols

Proof-of-stake blockchain protocols are becoming one of the most promising alternatives to the energy-consuming proof-of-work protocols. However, one particularly critical threat in the PoS setting is the well-known long-range attacks caused by secret key leakage (LRSL attack). Specifically, an adversary can attempt to control/compromise accounts possessing substantial stake at some past moment such that double-spend or erase past transactions, violating the fundamental persistence property of blockchain. Puncturable signatures provide a satisfying solution to construct practical proof-of-stake blockchain resilient to LRSL attack, despite of the fact that existent constructions are not efficient enough for practical deployments. In this paper, we provide an in-depth study of puncturable signatures and explore its applications in the proof-of-stake blockchain. We formalize a security model that allows the adversary for adaptive signing and puncturing queries, and show a construction with efficient puncturing operations based on the Bloom filter data structure and strong Diffie-Hellman assumption. The puncturing functionality we desire is for a particular part of message, like prefix, instead of the whole message. Furthermore, we use puncturable signatures to construct practical proof-of-stake blockchain protocols that are resilient to LRSL attack, while previously the forward-secure signature is used to immunize this attack. We implement our scheme and provide experimental results showing that in comparison with the forward-secure signature, our construction performs substantially better on signature size, signing and verification efficiency, significantly on key update efficiency.
Effects of contextual information on maintenance effort: A controlled experiment

There has been an increased focus on context-aware tools in software engineering. Within this area, an important challenge is to define and model the context for software-development projects and software development in general. This article reports a controlled experiment that compares the effort to implement changes, the correctness and the maintainability of an existing application between two projects; one that uses qualitative dashboards depicting contextual information, and one that does not. The results of this controlled experiment suggest that the usage of qualitative dashboards improves the correctness during the software maintenance activities and reduces the effort to implement these activities.
Coherence Criterion for Security Architecture of Digital Control System

The security of the digital Instrumentation and Control (I&C) system of the Nuclear Power Plant depends as on external security as on internal security. The security of the operational environment on any life cycle stage forms the external security. The external security includes a set of security controls for the development and engineering stages; else it includes the organizational and physical protection of the I&C system on site. The internal security commonly depends on the I&C system security policy and how correct it will be reflected in the security architecture of the I&C system. The security modeling might be successfully applied to harden as external as internal security. The paper is targeting the following security activities as: the partitioning the system architecture into several interconnected subsystems and components; evaluation of the communication pathways; establishing subsystem boundaries. The paper outlines the mathematical foundations to formalize the above-mentioned activities.