Reviewerly/exHarmony huggingface dataset:
A Conceptual Model to Predict Social Engineering Victims

Social engineering (SE) attacks are a serious threat to online users and might subject people to different kinds of harm. Despite increased concern with this risk, there has been little research activity focused upon social engineering in the potentially rich hunting ground of social networks. The number of victims of social engineering attacks will be decreased if the users' detection ability has improved. Yet, this improvement of the user's detection behaviour can't be occurred without investigating the users' weakness points. The present study develops a conceptual model to test the factors that influence social networks users' judgment of social engineering-based attacks in order to identify the weakest points of users' detection behaviour which also help to predict vulnerable individuals.
Secure Comparison Protocol with Encrypted Output and the Computation for Proceeding 2 Bits-by-2 Bits

A secure comparison protocol computes a comparison result between private information from inputs without leakage of the information. It is a very important factor in many potential applications such as secure multi-party computation. These protocols under Yao’s Millionaires’ Problem output a plaintext of a comparison result. Because of this feature, however, these protocols are not suitable for some applications such as secure biometrics, secure statistics and so on. From this concern, we focus on a secure comparison protocol whose output is one bit encrypted comparison result. In recent works, the computation of such protocols proceeds bit-by-bit. For this reason, these protocols still have a problem about the efficiency. In this paper, as a first step of our study, we propose two secure comparison protocols with encrypted output. As an interesting feature, the computation of one of our protocols proceeds 2 bits-by-2 bits. We prove the correctness of our protocols and estimate the computational cost. Moreover we discuss the security of our protocols against semi-honest model.
Homography Estimation Based on Error Elliptical Distribution

How to estimate accurately the homography is always a challenging problem in computer vision. In the reported literature, the measurement error of the image points is usually assumed to obey isotropic Gaussian distribution. However, real data very seldom follows this assumption. This paper proposes an estimation of homography under the assumption of image point errors following elliptical distribution, which is more coincident with real data. In the proposed method, the adaptive-scale elliptical residual kernel consensus (ASERKC) robust estimator is used to filter out inliers which are utilized to compute homography. Then, the elliptical weighted L-M (EW L-M) algorithm is optimized the homography. The experimental results show that the proposed method may present a more accurate homography. Especially when we applied it to incremental structure-from-motion (SFM), we find that the exact homography matrix is useful to select a better initial image pairs which can help obtain a more complete 3D points cloud.
Fast Sampling of Graph Signals with Noise via Neumann Series Conversion

Graph sampling with independent noise towards minimum mean square error (MMSE) leads to the known A-optimality criterion, which is computation-intensive to evaluate and NP-hard to optimize. In this paper, we propose a new low-complexity sampling strategy based on Neumann series that circumvents large matrix inversion and eigen-decomposition. We first prove that a DC-shifted A-optimality criterion is equivalent to an objective computed using the inverse of a sub-matrix of an ideal graph low-pass (LP) filter. The LP filter matrix can be approximated efficiently via fast Graph Fourier Transform (FGFT). Using the shifted A-optimality objective as a proxy, we then propose a fast algorithm to greedily select samples one-by-one based on a matrix inversion lemma with simple matrix updates. We show that the obtained solution has a performance upper bound via super-modularity analysis. Simulation results show that our proposed sampling strategy has lower complexity and outperforms several existing deterministic sampling schemes.
Core Partitioning: K-means and Similarity Clustering

K-means is arguably the most popular cluster-analysis method. The method’s output is twofold: (1) a partition of the entity set into clusters, and (2) centers representing the clustersCenter of cluster . The method is rather intuitive and usually requires just a few pages to get presented. In contrast, this text includes a number of less popular subjects that are much important when using K-means for real-world data analysis: Versions of K-means such as incremental K-means, nature inspired K-means, and entity-center “medoid” methods are presented. Three modifications of K-means onto different cluster structures are given: Fuzzy K-means for finding fuzzy clusters, Expectation-Maximization (EM) for finding probabilistic clusters, and Kohonen’s self-organizing maps (SOM) that tie up the sought clusters to a visually convenient two-dimensional grid. An equivalent reformulation of K-means criterion is described to yield what we call the complementary criterion. This criterion allows to reinterpret the method as that for finding big anomalous clusters. In this formulation, K-means is shown to extend the Principal component analysis criterion to the case at which the scoring factors are supposed to be binary. This allows to address a haunting issue at K-means, finding the “right” number of clusters K, by one-by-one building Anomalous clusters. Section 4.6 is devoted to partitioning over similarity data. First of all, the complementary K-means criterion is equivalently reformulated as the so-called semi-average similarity criterion. This criterion is maximized with a consecutive merger process referred to as SA-Agglomeration clustering to produce provably tight, on average, clusters. This method stops merging clusters when the criterion does not increase anymore if the data has been pre-processed by zeroing the similarities of the objects to themselves. A similar process is considered for another natural criterion, the summary within-cluster similarity, for which two pre-processing options are considered. These are: a popular “modularity” clustering option, based on subtraction of random interactions, and “uniform” partitioning, based on a scale shift, a.k.a. soft thresholding. Using either pre-processing option, the summary clustering also leads to an automated determination of the number of clusters. The chapter concludes with Sect. 4.7 on consensus clustering, a more recent concept. In the context of central partition for a given ensemble of partitions, two distance-between-partitions measures apply, both involving the so-called consensus matrix. The consensus similarity is defined, for any two objects, by the number of clusters in the ensemble to which both objects belong. This brings the issue of consensus into the context of similarity clustering, in the form of either the semi-average criterion or uniform partitioning criterion.
Analyzing Human Reaction Time for Talker Change Detection

The ability to detect a change in the input is an essential aspect of perception. In speech communication, we use this ability to identify "talker changes" when listening to conversational speech (such as, audio podcasts). In this paper, we propose to improve our understanding about how fast listeners detect a change in talker, and the acoustic features tracked to identify a voice by designing a novel experimental paradigm. A listening experiment is designed in which listeners indicate the moment of perceived talker change in multi-talker speech utterances. We examine talker change detection performance by probing the human reaction time (RT). A random forest regression is used to model the relationship between RTs and acoustic features. The findings suggest that: (i) RT is less than a second, (ii) RT can be predicted from the difference in acoustic features of segment before and after change, and (iii) there a exists a significant dependence of RT on MFCC-D1 (delta MFCCs) features between segments of speech before and after the change instant. Further, a comparison with a machine system designed for the same task of TCD using speaker diarization principles showed a poor performance relative to the humans.
GPU Based Horn-Schunck Method to Estimate Optical Flow and Occlusion

Optical flow is the apparent motion pattern of pixels in two consecutive images. Optical flow has many applications: navigation control of autonomous vehicles, video compression, noise suppression, and others. There are different methods to estimate the optical flow, where variational models are the most frequently used. These models state an energy model to compute the optical flow. These models may fail in presence of occlusions and illumination changes. In this work is presented a method that estimates the flow from the classical Horn-Schunk method and the incorporation of an occlusion layer that gives to the model the ability to handle occlusions. The proposed model was implemented in an Intel i7, 3.5 GHz, GPU GeForce NVIDIA-GTX-980-Ti, using a standard webcam. Using images of $$320\times 240$$ pixels we reached 4 images per second, i.e. this implementation can be used in an application like an autonomous vehicle.
A novel haze image enhancement algorithm

The atmospheric scattering model (ASM) is usually used in the haze image enhancement. Researchers always assume that the scene incident light is fixed, which is unreasonable in the real environment. In this paper, an improved atmospheric scattering model (IASM) with the incident light redefined is proposed to enhance the haze images. Firstly, the bright channel map and fuzzy clustering algorithm are used to divide the haze image into different scenes and estimate the incident light of each scene. Secondly, the atmospheric light is estimated based on the sky area location and the transmittance. Finally, the albedo of the image is restored through the IASM. The experiments show that the proposed algorithm is effective in enhancing the haze image.
A Novel Image Encryption Algorithm Using Multiple Encryption Techniques for Mobile Devices

Background &amp; Objective: Digital multimedia exchange between different mobile communication devices has increased rapidly with the invention of the high-speed data services like LTE-A, LTE, and WiMAX. However, there are always certain security risks associated with the use of wireless communication technologies. Methods: To protect the digital images against cryptographic attacks different image encryption algorithms are being employed in the wireless communication networks. These algorithms use comparatively less key spaces and accordingly offer inadequate security. The proposed algorithm described in this paper based on Rubik’s cube principle because of its high confusion and diffusion properties, Arnold function having effective scrambling power, blocking cipher with block encryption and permutation powers. The main strength of the proposed algorithm lies in the large key spaces and the combination of different high power encryption techniques at each stage of algorithm. The different operations employed on the image are with four security keys of different key spaces at multiple stages of the algorithm. Results &amp; Conclusion: Finally, the effectiveness and the security analysis results shows that the proposed image encryption algorithm attains high encryption and security capabilities along with high resistance against cryptanalytic attacks, differential attacks and statistical attacks.
A Third-Party Mobile Payment Scheme Based on NTRU Against Quantum Attacks

Mobile devices now serve to pay for goods and services by means of the transmission of data, a system known as mobile payments. Mobile payment is receiving growing attention globally, from consumers to merchants, as an alternative to using cash, check, or credit cards. Most encryption techniques applied in mobile payment are based on traditional public key infrastructure. However, the traditional public key encryption algorithm has higher requirements for hardware, which is not suitable for mobile terminals of limited computing resources. In addition, these public key encryption algorithms are vulnerable to quantum computing attacks and the availability of practical quantum computer is approaching faster than previously believed. Since mobile payment is facing more and more security issues, how to complete the payment process effectively and securely becomes a problem urgently to be solved. In this paper, we firstly analyze the problem of the signature scheme for mobile payment and propose an improved signature scheme based on the Number Theory Research Unit (NTRU). The results of the experiment show that the proposed signature scheme is secure and efficient which can raise the speed and probability of generating reasonable signature value. Second, we introduce a new public key infrastructure NTRU-WPKI and design a third-party mobile payment model based on NTRU-WPKI to increase the efficiency and security. Finally, by doing the overall simulation, we prove that our model is highly effective and secure.
A branch university journal as an information resource: the bibliometric analysis experience

One of the most widespread publications of a university is «Vestnik» (Bulletin), which reflects not only scientific and professional interests of the university in a certain subject field, but scientific and professional interests of the scientific community within its territory and abroad. The journal testifies to the branch science development in general, contributes to formation of socio-economic and scientific environment of a certain region, and the country as a whole. The article deals with the journal «Bulletin of Kemerovo State University of Culture and Arts» («Vestnik of KemGUKI») as an information resource of the branch university. The author studies main charac­teristics of the publication in the Russian Science Citation Index; concludes that the journal quality indicators (its index and place the Science Index rating, two-year and five-year impact factor) are con­stantly growing, that indicates promoting the publication in the professional community information environment. The paper highlights the bibliometric analysis results for 5 years (2013–2017) investigating publications (segments of document flow, micro-flows) grouped by features of content, species and author's structure. The journal reflects a wide range of humanitarian knowledge. The authors of «Vestnik KemGUKI» are highly qualified teachers, their scientific and educational activities develop the branch science and pro­fessional education and form the University positive image. «Vestnik» is a platform to present and test dissertators’ re-searches. The document flow analysis on the authors’ affiliations allowed identifying teams that work in line with the interests of KemGUCA. Among them are not only universities of culture and other humanitarian educational institutions, but organizations not related to educational activities. Studying the authors’ regional affiliation, the paper reflects the geographical landscape wideness. The authors are not only from Kemerovo, as well as from a large number of regional centers, cities and republics. Thanks to scientists and teachers of the neighboring countries (Ukraine, Belarus, Kazakhstan, Kyrgyzstan) the range of authors is constantly expanding. The study reveals coincidence of scholar interests with the scientific educational community of foreign countries (Poland, Bulgaria, Germany, China, India). It shows that «Vestnik» is recognized in the professional environment of the humanitarian community as a resource promoting education, science and culture in Kuzbass, Siberia, Russia, and the near and far abroad.
From Bi-Level Sparse Clustering to Deep Clustering

Many clustering methods highly depend on extracted features. We propose a joint optimization framework in terms of both feature extraction and discriminative clustering. We utilize graph regularized sparse codes as the features, and formulate sparse coding as the constraint for clustering. Two cost functions are developed based on entropy-minimization and maximum-margin clustering principles, respectively. They are considered as the objectives to be minimized. Solving such a bi-level optimization mutually reinforces both sparse coding and clustering steps. Experiments on several benchmark datasets verify remarkable performance improvements led by the proposed joint optimization. While sparse coding-based clustering methods have shown to be successful, their bottlenecks in both efficiency and scalability limit the practical usage. In recent years, deep learning has been proved to be a highly effective, efficient and scalable feature learning tool. We propose to emulate the sparse coding-based clustering pipeline in the context of deep learning, leading to a carefully crafted deep model benefiting from both. A feed-forward network structure, named TAGnet, is constructed based on a graph-regularized sparse coding algorithm. It is then trained with task-specific loss functions from end to end. We discover that connecting deep learning to sparse coding benefits not only the model performance, but also its initialization and interpretation. Moreover, by introducing auxiliary clustering tasks to the intermediate feature hierarchy, we formulate DTAGnet and obtain a further performance boost. Extensive experiments demonstrate that the proposed model gains remarkable margins over several state-of-the-art methods.
A Method for Recognizing Postures and Gestures Using Foot Pressure Sensors

In this paper, we propose a method for recognizing postures and gestures by using foot pressure sensors, and we investigate optimal positions for pressure sensors on soles from the viewpoint of motion recognition accuracy. In experiments, the recognition accuracies of 22 kinds of daily postures and gestures were evaluated from foot-pressure sensor values. Furthermore, the optimum measurement points for high recognition accuracy were examined by evaluating combinations of two foot pressure measurement areas on a round-robin basis. As a result, when selecting the optimum two points for each user, the recognition accuracy was about 94.5% on average. The recognition accuracy of the averaged combinations of the best two combinations for all subjects was classified with an accuracy of about 91.9% on average. As a result of an evaluation to raise versatility, the average recognition accuracy in a three-point evaluation was 98.4%, which was almost the same with the recognition accuracy when using all 105 points. In anticipation of the applicability of this research result, two types of pressure sensing shoes were developed.
Obstacle avoidance for quadrotors using reinforcement learning and obstacle-airflow interactions

This research investigates and proposes a new method for obstacle detection and avoidance on quadrotors, that relies solely on measurements from the accelerometer and rotor controllers. The detection of obstacles is based on the principle that the airflow around a quadrotor changes when the quadrotor is flying near a surface. A well-known example of this is the ground effect, an increase in lift force close to a ground surface. Similarly, a change in dynamics occurs when a quadrotor is flying close to a wall or ceiling. The proposed method uses a reinforcement learning controller to detect obstacles based on these measurements, and takes action to lead the quadrotor back to safety. A proof-of-concept of this method is developed by training a reinforcement learning agent to avoid obstacles beneath a descending quadrotor. This is first done in a simulated environment, where the influence of hyperparameters, the amount of noise in the state signal, and the number of training episodes are investigated. The best performing agent from simulation is evaluated during a flight experiment with the Parrot Bebop 1 drone, where it is able to prevent the quadrotor from hitting the obstacle in 80% of the episodes. Furthermore, it is shown that the same level of performance can be achieved, by learning fully from scratch, in-flight, without prior knowledge or training, during 50 real flight training episodes. An approach for extending this method to the avoidance of walls, ceilings, and smaller obstacles is discussed. Additionally, it is shown that this method can easily be extended to other quadrotors.
The Biometric Characteristics of a Smile

Facial expressions have been studied looking for its diagnostic capabilities in mental health and clues for longevity, gender and other such personality traits. The use of facial expressions, especially the expression of smile, as a biometric has not been looked into great detail. However, research shows that a person can be identified from their behavioural traits including their emotional expressions. In this Chapter, we discuss a novel computational biometric model which can be derived from the smile expression. We discuss how the temporal components of a smile can be utilised to show that similarities in the smile exist for an individual and it can be enabled to create a tool which can be utilised as a biometric.
Real-Time Mobile Assessment of Learning

Real-time mobile assessment (RTMA) can function as assistive technology, since the anonymity of feedback promotes active participation in lessons without being exposed to criticism. This field experiment explored the impact of RTMA, beyond a whole-class technology, on academic achievement, students' perceived learning, academic self-efficacy, learning motivation, and self-esteem among middle school students. The participants were 80 ninth graders in the excellence, mainstream, and remedial tracks being taught by the same experienced language arts teacher. The experiment included nine double language lessons with a random assignment of the technological conditions and the counterbalance procedure of the topics studied. The findings suggested that students in the remedial track benefit from using RTMA in terms of achievement, perceived learning, self-efficacy, and motivation. Students in the excellence track benefit from RTMA in terms of intrinsic motivation, and students in the mainstream track in terms of self-esteem. Implication for educational research and practice are discussed.
A rank correlation coefficient based particle filter to estimate parameters in non-linear models

Particle filtering algorithm has found an increasingly wide utilization in many fields at present, especially in non-linear and non-Gaussian situations. Because of the particle degeneracy limitation, various resampling methods have been researched. The estimation process of particle filtering algorithm is a series of weighted calculation processes, which can be regarded as weighted data fusion. This article proposed an improved particle filtering algorithm combining with different rank correlation coefficients to overcome the shortcomings of degeneracy. By simulating iteration operation in MATLAB, it discovers that the proposed algorithm provides better accuracy in comparison with particle filtering, Gaussian sum particle filter, and Gaussian mixture sigma-point particle filter in Gaussian mixture noise. A practical seven-dimensional harmonic model is also implemented in the simulation. After comparing the performances of different algorithms, we found that the proposed method had more accuracy than the widely used extended Kalman filtering algorithm.