Reviewerly/exHarmony huggingface dataset:
VRSA Net: VR Sickness Assessment Considering Exceptional Motion for 360° VR Video

The viewing safety is one of the main issues in viewing virtual reality (VR) content. In particular, VR sickness could occur when watching immersive VR content. To deal with the viewing safety for VR content, objective assessment of VR sickness is of great importance. In this paper, we propose a novel objective VR sickness assessment (VRSA) network based on deep generative model for automatically predicting the VR sickness score. The proposed method takes into account motion patterns of VR videos in which an exceptional motion is a critical factor inducing excessive VR sickness in human motion perception. The proposed VRSA network consists of two parts, which are VR video generator and VR sickness score predictor. By training the VR video generator with common videos with non-exceptional motion, the generator learns the tolerance of VR sickness in human motion perception. As a result, the difference between the original and the generated videos by the VR video generator could represent exceptional motion of VR video causing VR sickness. In the VR sickness score predictor, the VR sickness score is predicted by projecting the difference between the original and the generated videos onto the subjective score space. For the evaluation of VR sickness assessment, we built a new dataset which consists of 360° videos (stimuli), corresponding physiological signals, and subjective questionnaires from subjective assessment experiments. Experimental results demonstrated that the proposed VRSA network achieved a high correlation with human perceptual score for VR sickness.
Mining weighted frequent sequences in uncertain databases

Frequent pattern mining has become very useful and interesting to researchers due to its high applicability. Different real-life databases (e.g., sensor network, medical diagnosis data) are uncertain in their nature. Many algorithms have been developed to mine the frequent uncertain patterns based on expected support values. Nonetheless, those are circumscribed to find the frequent patterns by using some filtering constraints. Moreover, it is challenging to find the actual interesting patterns as different patterns carry different importance. In this work, a new framework is proposed to mine sequences in uncertain databases satisfying both weight and support constraints. Subsequently, an efficient algorithm (uWSequence) is developed to discover the uncertain weighted sequences. In addition, the pruning measures iMaxPr, and expSupporttop play a vital role to make uWSequence remarkably time-efficient, by filtering out the unfavorable patterns in early stages. The applicability of this proposed framework is shown to solve various problems (e.g., weather forecasting, sensor-based event findings). To our knowledge, ours is the first work on weighted sequences in uncertain databases. Extensive performance analysis confirms the efficiency of the proposed algorithm as well as the superiority over the existing algorithms.
Revocable attribute-based encryption with decryption key exposure resistance and ciphertext delegation

Attribute-based encryption (ABE) enables fine-grained access control over encrypted data. A practical and popular approach for handing revocation in ABE is to use the indirect revocation mechanism, in which a key generation centre (KGC) periodically broadcasts key update information for all data users over a public channel. Unfortunately, existing RABE schemes are vulnerable to decryption key exposure attack which has been well studied in the identity-based setting. In this paper, we introduce a new notion for RABE called re-randomizable piecewise key generation by allowing a data user to re-randmomize the combined secret key and the key update to obtain the decryption key, and the secret key is unrecoverable even both the decryption key and the key update are known by the attacker. We then propose a new primitive called re-randomizable attribute-based encryption (RRABE) that can achieve both re-randomizable piecewise key generation and ciphertext delegation. We also refine the existing security model for RABE to capture decryption key exposure resistance and present a generic construction of RABE from RRABE. Finally, by applying our generic transformation, we give a concrete RABE scheme achieving decryption key exposure resistance and ciphertext delegation simultaneously.
Attractive-and-repulsive center-symmetric local binary patterns for texture classification

Aiming at the defect of Local Binary Pattern (LBP) and its variants, this paper presents a new modeling of the conventional LBP operator for texture classification. Named Attractive-and-Repulsive Center-Symmetric Local Binary Patterns (ACS-LBP and RCS-LBP), the proposed new texture descriptors preserve the advantageous characteristics of uniform LBP. Based on local attractive-and-repulsive characteristics, the proposed local texture modeling can really inherit good properties from both gradient and texture operators than the Center-Symmetric Local Binary Patterns (CS-LBP) does. Different from CS-LBP, which considers four doublets around the center pixel, the proposed methods take into account the four triplets corresponding to the vertical and horizontal directions, and the two diagonal directions by including the value of the central pixel in the modeling. In addition, Average Local Gray Level (ALGL), Average Global Gray Level (AGGL) and the median value over 3 × 3 neighborhood are introduced to capture both microstructure and macrostructure texture information. To capture the coarse and fine information of the features and thus to make ACS-LBP and RCS-LBP more robust and stable, multiscale ARCS-LBP descriptor is proposed. There is no necessity to learn texton dictionary, as in methods based on clustering, and no tuning of parameters is required to deal with different datasets. Extensive experiments performed on thirteen challenging representative texture databases show that the proposed operators can achieve impressive classification accuracy. Furthermore, we clearly validate the feasibility of the proposed ACS-LBP, RCS-LBP and ARCS-LBP descriptors by comparing their results with those obtained with a large number of recent state-of-the-art texture descriptors including deep features. Statistical significance of achieved accuracy improvement is demonstrated through Wilcoxon signed rank test.
Computing With Networks of Oscillatory Dynamical Systems

As we approach the end of the silicon road map, alternative computing models that can solve at-scale problems in the data-centric world are becoming important. This is accompanied by the realization that binary abstraction and Boolean logic, which have been the foundations of modern computing revolution, fall short of the desired performance and power efficiency. In particular, hard computing problems relevant to pattern matching, image and signal processing, optimizations, and neuromorphic applications require alternative approaches. In this paper, we review recent advances in oscillatory dynamical system-based models of computing and their implementations. We show that simple configurations of oscillators connected using simple electrical circuits can result in interesting phase and frequency dynamics of such coupled oscillatory systems. Such networks can be controlled, programmed, and observed to solve computationally hard problems. Although our discussion in this paper is limited to insulator-to-metal transition devices and spin-torque oscillators, the general philosophy of such a computing paradigm of “let physics do the computing” can be translated to other mediums as well, including micromechanical and optical systems. We present an overview of the mathematical treatments necessary to understand the time evolution of these systems and highlight the recent experimental results in this area that suggest the potential of such computational models.
ToRank: Identifying the most influential suspicious domains in the Tor network

The Tor network hosts a significant amount of hidden services related to suspicious activities. Law Enforcement Agencies need to monitor and to investigate crimes hidden behind the anonymity provided by Tor. In this paper, we propose a new algorithm, named ToRank, that ranks hidden services in Tor better than the known algorithms used for the Surface Web. We also thoroughly analyze the content present in Tor, creating a dataset, DUTA-10K, that extends the previous Darknet Usage Text Address (DUTA) dataset. We quantitatively compared ToRank with some of the most popular ranking algorithms, like PageRank, HITS, and Katz. Results showed that our proposal obtains a higher harm to the Tor network robustness than all of them, what indicates its superiority for this problem. The analysis of DUTA-10K reveals that only 20% of the hidden services that can be accessed are related to suspicious activities, and 48% are associated with normal ones. We also discovered that domains related to suspicious activities usually present multiple clones under different addresses, what could be used as an additional feature for identifying them. We consider that our new algorithm, the extended dataset, and the findings obtained from the analysis carried out are helpful for LEAs to fight against crimes that take place in the Tor hidden services.
Locality constrained representation-based K-nearest neighbor classification

K-nearest neighbor rule (KNN) is one of the most widely used methods in pattern recognition. However, the KNN-based classification performance is severely affected by the sensitivity of the neighborhood size k and the simple majority voting in the regions of k-neighborhoods, especially in the case of the small sample size with the existing outliers. To overcome the issues, we propose two locality constrained representation-based k-nearest neighbor rules with the purpose of further improving the KNN-based classification performance. The one is the weighted representation-based k-nearest neighbor rule (WRKNN). In the WRKNN, the test sample is represented as the linear combination of its k-nearest neighbors from each class, and the localities of k-nearest neighbors per class as the weights constrain their corresponding representation coefficients. Using the representation coefficients of k-nearest neighbors per class, the representation-based distance between the test sample and the class-specific k-nearest neighbors is calculated as the classification decision rule. The other one is the weighted local mean representation-based k-nearest neighbor rule (WLMRKNN). In the WLMRKNN, k-local mean vectors of k-nearest neighbors per class are first calculated and then used for representing the test sample. In the linear combination of the class-specific k-local mean vectors to represent the test sample, the localities of k-local mean vectors per class are considered as the weights to constrain the representation coefficients of k-local mean vectors. The representation coefficients are employed to design the classification decision rule which is the class-specific representation-based distance between the test sample and k-local mean vectors per class. To demonstrate the effectiveness of the proposed methods, we conduct extensive experiments on the UCI and UCR data sets and face databases, in comparisons with seven related competitive KNN-based methods. The experimental results show that the proposed methods perform better with less sensitiveness to k, especially in the small sample size cases.
Efficient and Provably Secure Certificateless Parallel Key-Insulated Signature Without Pairing for IIoT Environments

With the continuous development of the industrial Internet of Things (IIoT), many organizations opt for storing the data collected by smart devices on a cloud server for saving costs. Considering the untrusted nature of the communication channel, how to ensure the authenticity of data is an urgent matter to be solved. Certificateless signatures that can provide an authentication mechanism for the data seem to be a viable option to this problem. Nevertheless, the certificateless signature scheme proposed so far is either easy to be broken or inefficient. In this paper, a pairing-free and provably secure certificateless parallel key-insulated signature (CL-PKIS) scheme is put forward for securing the communication in the IIoT environment. Unlike previous work in this field, our scheme not only uses elliptic curves to maintain higher efficiency, but also implants key-insulated primitive to reduce the risk of key exposures. Besides, our scheme provides a parallel mechanism to make it more suitable for the IIoT environment, which was not available in previous solutions. Finally, the security of our CL-PKIS scheme is proved under the discrete logarithm assumption in the random oracle model.
Hot Topic Detection Based on a Refined TF-IDF Algorithm

In this paper, we propose a refined term frequency inversed document frequency (TF-IDF) algorithm called TA TF-IDF to find hot terms, based on time distribution information and user attention.We also put forward a method to generate new terms and combined terms, which are split by the Chinese word segmentation algorithm.Then, we extract hot news according to the hot terms, grouping them into K-means clusters so as to realize the detection of hot topics in news.The experimental results indicated that our method based on the refined TF-IDF algorithm can find hot topics effectively.
Parallel Simulated Annealing with a Greedy Algorithm for Bayesian Network Structure Learning

We present a hybrid algorithm called parallel simulated annealing with a greedy algorithm (PSAGA) to learn Bayesian network structures. This work focuses on simulated annealing and its parallelization with memoization to accelerate the search process. At each step of the local search, a hybrid search method combining simulated annealing with a greedy algorithm was adopted. The proposed PSAGA aims to achieve both the efficiency of parallel search and the effectiveness of a more exhaustive search. The Bayesian Dirichlet equivalence metric was used to determine an optimal structure for PSAGA. The proposed PSAGA was evaluated on seven well-known Bayesian network benchmarks generated at random. We first conducted experiments to evaluate the computational time performance of the proposed parallel search. We then compared PSAGA with existing variants of simulated annealing-based algorithms to evaluate the quality of the learned structure. Overall, the experimental results demonstrate that the proposed PSAGA shows better performance than the alternatives in terms of computational time and accuracy.
Semiautomatic first-arrival picking of microseismic events by using the pixel-wise convolutional image segmentation method

Microseismic imaging plays an important role in hydraulic fracture detection, and the first-arrival picking of microseismic events is the bedrock of microseismic imaging. Manual picking is the most reliable and also the most time-consuming method for the detection of the first arrival of microseismic events. Accurate and efficient first-arrival picking in a real noisy environment is a challenge for most of the automatic first-arrival picking methods. We have developed a novel workflow to automatically pick the first arrival of microseismics by using a state-of-the art pixel-wise convolutional image segmentation method. We first form the training data by randomly selecting part of the microseismic traces and manually pick the time index of the first arrivals. Next, we segment the selected traces into two parts according to the time index of manual picking and assign each part a label accordingly. Then, we build an encoder-decoder convolutional neural network architecture and use the training data and training label as the input. Next, we obtain the trained network hierarchy by learning the segmented training data and labels. Finally, we predict the first arrivals of microseismic events by applying the trained network hierarchy to the rest of the microseismic traces. The synthetic and field data examples demonstrate that our method successfully identifies the first arrivals. The predicted first-arrival result obtained by using our method is superior to the result obtained by using the traditional method of short-term average and long-term average.
A real-time service system in the cloud

Recently, we have witnessed unprecedented use of cloud computing and its services. It is influencing the way software is built, as well as company' resources such as servers, workstations or generally hardware are used. This paper aims to examine the benefits of cloud usage to support real-time service systems, using the Salesforce platform. First, we explore the meaning and the role of cloud computing for the real-time service systems efficient functioning. Then, we build a service management platform for the Polish Billiards and Snooker Association (PBSA), based on a real-time system located in the cloud. This way, PBSA managers are able to complete their tasks in this system on-demand. Moreover, it is set up as a private cloud to grant access only to the snooker organization employees.
A Summary of Research on Blockchain in the Field of Intellectual Property

With the continuous development and application of blockchain technology, the academic and commercial circles are constantly exploring the research directions and practical applications of blockchains. Today, in the financial, sales, medical and other fields, the blockchain has already played its advantages. In this paper, we focus on the related research and applications of blockchain technology in the field of intellectual property, analyze the academic research and commercial application in this direction, and try to provide a new feasible direction for the research and development of the blockchain in the next stage.
A survey of clustering algorithms for an industrial context

Across a wide variety of fields and especially for industrial companies, data are being collected and accumulated at a dramatic pace from many different resources and services. Hence, there is an urgent need for a new generation of computational theories and tools to assist humans in extracting useful information from the rapidly growing volumes of digital data. A well-known fundamental task of data mining to extract information is clustering. However, with the modified applications for various domains, several researchers have developed and have provided many clustering algorithms. This complexity makes it difficult for researchers and practitioners to keep up with clustering algorithms development. As a result, finding appropriate algorithms helps significantly to organize information and extract the correct answer from different queries of the databases. In this respect, the aim of this paper is to find the appropriate clustering algorithm for sparse industrial dataset. To achieve this goal, we first present related work that focus on comparing different clustering algorithms over the past twenty years. After that, we provide a categorization of different clustering algorithms found in the literature by matching their properties to the 4V’s challenges of Big data which allow us to select the candidate clustering algorithm. Finally, using internal validity indices, K-means, agglomerative hierarchical, DBSCAN and SOM have been implemented and compared on four datasets. In addition, we highlighted the best performing clustering algorithm that gives us the efficient clusters for each dataset.
Online Public Shaming on Twitter: Detection, Analysis, and Mitigation

Public shaming in online social networks and related online public forums like Twitter has been increasing in recent years. These events are known to have a devastating impact on the victim's social, political, and financial life. Notwithstanding its known ill effects, little has been done in popular online social media to remedy this, often by the excuse of large volume and diversity of such comments and, therefore, unfeasible number of human moderators required to achieve the task. In this paper, we automate the task of public shaming detection in Twitter from the perspective of victims and explore primarily two aspects, namely, events and shamers. Shaming tweets are categorized into six types: abusive, comparison, passing judgment, religious/ethnic, sarcasm/joke, and whataboutery, and each tweet is classified into one of these types or as nonshaming. It is observed that out of all the participating users who post comments in a particular shaming event, majority of them are likely to shame the victim. Interestingly, it is also the shamers whose follower counts increase faster than that of the nonshamers in Twitter. Finally, based on categorization and classification of shaming tweets, a web application called BlockShame has been designed and deployed for on-the-fly muting/blocking of shamers attacking a victim on the Twitter.
MSCryptoNet: Multi-Scheme Privacy-Preserving Deep Learning in Cloud Computing

Privacy in the Internet of Things is a fundamental challenge for the Ubiquitous healthcare systems that depend on the data aggregated and collaborative deep learning among different parties.This paper proposes the MSCryptoNet, a novel framework that enables the scalable execution and the conversion of the state-of-the-art learned neural network to the MSCryptoNet models in the privacy-preservation setting.We also design a method for approximation of the activation function basically used in the convolutional neural network (i.e., Sigmoid and Rectified linear unit) with low degree polynomials, which is vital for computations in the homomorphic encryption schemes.Our model seems to target the following scenarios: 1) the practical way to enforce the evaluation of classifier whose inputs are encrypted with possibly different encryption schemes or even different keys while securing all operations including intermediate results and 2) the minimization of the communication and computational cost of the data providers.The MSCryptoNet is based on the multi-scheme fully homomorphic encryption.We also prove that the MSCryptoNet as a privacy-preserving deep learning scheme over the aggregated encrypted data is secured.
Deterrence and prevention-based model to mitigate information security insider threats in organisations

Previous studies show that information security breaches and privacy violations are important issues for organisations and people. It is acknowledged that decreasing the risk in this domain requires consideration of the technological aspects of information security alongside human aspects. Employees intentionally or unintentionally account for a significant portion of the threats to information assets in organisations. This research presents a novel conceptual framework to mitigate the risk of insiders using deterrence and prevention approaches. Deterrence factors discourage employees from engaging in information security misbehaviour in organisations, and situational crime prevention factors encourage them to prevent information security misconduct. Our findings show that perceived sanctions certainty and severity significantly influence individuals' attitudes and deter them from information security misconduct. In addition, the output revealed that increasing the effort, risk and reducing the reward (benefits of crime) influence the employees' attitudes towards prevent information security misbehaviour. However, removing excuses and reducing provocations do not significantly influence individuals' attitudes towards prevent information security misconduct. Finally, the output of the data analysis also showed that subjective norms, perceived behavioural control and attitude influence individuals' intentions, and, ultimately, their behaviour towards avoiding information security misbehaviour.