Reviewerly/exHarmony huggingface dataset:
Eye movement analysis with switching hidden Markov models

Here we propose the eye movement analysis with switching hidden Markov model (EMSHMM) approach to analyzing eye movement data in cognitive tasks involving cognitive state changes. We used a switching hidden Markov model (SHMM) to capture a participant’s cognitive state transitions during the task, with eye movement patterns during each cognitive state being summarized using a regular HMM. We applied EMSHMM to a face preference decision-making task with two pre-assumed cognitive states—exploration and preference-biased periods—and we discovered two common eye movement patterns through clustering the cognitive state transitions. One pattern showed both a later transition from the exploration to the preference-biased cognitive state and a stronger tendency to look at the preferred stimulus at the end, and was associated with higher decision inference accuracy at the end; the other pattern entered the preference-biased cognitive state earlier, leading to earlier above-chance inference accuracy in a trial but lower inference accuracy at the end. This finding was not revealed by any other method. As compared with our previous HMM method, which assumes no cognitive state change (i.e., EMHMM), EMSHMM captured eye movement behavior in the task better, resulting in higher decision inference accuracy. Thus, EMSHMM reveals and provides quantitative measures of individual differences in cognitive behavior/style, making a significant impact on the use of eyetracking to study cognitive behavior across disciplines.
Datacenter congestion control

Recent years have seen a slew of papers on datacenter congestion control mechanisms. In this editorial, we ask whether the bulk of this research is needed for the common case where congestion control involves hosts responding to simple congestion signals from the network and the performance goal is reducing some average measure of flow completion time. We raise this question because we find that, out of all the possible variations one could make in congestion control algorithms, the most essential feature is the switch scheduling algorithm. More specifically, we find that congestion control mechanisms that use Shortest-Remaining-Processing-Time (SRPT) achieve superior performance as long as the rate-setting algorithm at the host is reasonable. We further find that while SRPT's performance is quite robust to host behaviors, the performance of schemes that use scheduling algorithms like FIFO or Fair Queuing depend far more crucially on the rate-setting algorithm, and their performance is typically worse than what can be achieved with SRPT. Given these findings, we then ask whether it is practical to realize SRPT in switches without requiring custom hardware. We observe that approximate and deployable SRPT (ADS) designs exist, which leverage the small number of priority queues supported in almost all commodity switches, and require only software changes in the host and the switches. Our evaluations with one very simple ADS design shows that it can achieve performance close to true SRPT and is significantly better than FIFO. Thus, the answer to our basic question - whether the bulk of recent research on datacenter congestion control algorithms is needed for the common case - is no.
A Comprehensive Review of Keystroke Dynamics-Based Authentication Mechanism

Keystroke dynamics, also called keystrokeRaul, Nataasha biometricsShankarmani, Radha or typingJoshi, Padmaja dynamics, is a biometric-based on typing style. Typists have unique typing patterns that can be analyzed to confirm the authenticity of the user. Keystroke dynamics is most often applied in situations where the authenticity of a user must be ascertained with extreme confidence. It could be used as an additional degree of security for password-protected applications. If user’s password is compromised, and the keystroke dynamics of the real user is known, the application may be able to reject the impostor despite having received valid credentials. Different types of keyboards and remote access are major problems of keystroke dynamics authentication technique. In this paper, a comprehensive analysis of contemporary work on keystroke dynamic authentication mechanisms is summarized to analyze the effectiveness of various methodologies in present. Also, various statistical-based and machine learning-based algorithms are analyzed with their strengths and weaknesses. From this survey, it was observed that there is a need to strengthen the keystroke dynamics dataset which has all essential features. Also an efficient algorithm is required to obtain high accuracy to make authentication effective, as the performance of biometric keystroke authentication is still an open research.
A hybrid improved whale optimization algorithm

Aiming at the problems that whale optimization algorithm is easy to fall into local optimal, slow convergence speed and low accuracy in the complex environment of multi-variable processing, it has always been challenging in practical application. In this paper, a new hybrid modified whale optimization algorithm (HIWOA) is proposed, which adds a new feedback mechanism to improve population diversity and reduce the possibility of falling into local optimization. The nonlinear convergence factor and inertia weight coefficient are used to improve the updating of whale individual position and improve the speed of convergence and accuracy. Simulation experiments were carried out on 23 benchmark functions, and the results showed that compared with the original WOA algorithm and the other three improved algorithms in the last two years, the HIWOA algorithm was more competitive in accuracy of solution, convergence speed and stability.
Tetrapartite entanglement features of W-Class state in uniform acceleration

Using the single-mode approximation, we first calculate entanglement measures such as negativity (1–3 and 1–1 tangles) and von Neumann entropy for a tetrapartite W-Class system in noninertial frame and then analyze the whole entanglement measures, the residual π4 and geometric Π4 average of tangles. Notice that the difference between π4 and Π4 is very small or disappears with the increasing accelerated observers. The entanglement properties are compared among the different cases from one accelerated observer to four accelerated observers. The results show that there still exists entanglement for the complete system even when acceleration r tends to infinity. The degree of entanglement is disappeared for the 1–1 tangle case when the acceleration r > 0.472473. We reexamine the Unruh effect in noninertial frames. It is shown that the entanglement system in which only one qubit is accelerated is more robust than those entangled systems in which two or three or four qubits are accelerated. It is also found that the von Neumann entropy S of the total system always increases with the increasing accelerated observers, but the Sκξ and Sκζδ with two and three involved noninertial qubits first increases and then decreases with the acceleration parameter r, but they are equal to constants 1 and 0.811278 respectively for zero involved noninertial qubit.
Multi-view Locality Low-rank Embedding for Dimension Reduction

During the last decades, we have witnessed a surge of interest in learning a low-dimensional space with discriminative information from one single view. Even though most of them can achieve satisfactory performance in certain situations, they fail to fully consider the information from multiple views which are highly relevant but sometimes look different from each other. Besides, correlations between features from multiple views always vary greatly, which challenges the capability of multi-view subspace learning methods. Therefore, how to learn an appropriate subspace which could maintain valuable information from multi-view features is of vital importance but challenging. To tackle this problem, this paper proposes a novel multi-view dimension reduction method named Multi-view Locality Low-rank Embedding for Dimension Reduction (MvL2E). MvL2E mainly focuses on capturing a common low-dimensional embedding among multiple different views, which makes full use of correlations between multi-view features by adopting low-rank representations. Meanwhile, it aims to maintain the correlations and construct a suitable manifold structure to capture the low-dimensional embedding for multi-view features. A centroid based scheme is designed to get one common low-dimensional manifold space and force multiple views to learn from each other. And an iterative alternating strategy is developed to obtain the optimal solution of MvL2E. The proposed method is evaluated on 5 benchmark datasets. Comprehensive experiments show that our proposed MvL2E can achieve comparable performance with previous approaches proposed in recent works of literature.
Investigating the use of Different Visual Cues to Improve Social Presence within a 360 Mixed Reality Remote Collaboration*

In this paper, we investigate the social aspects and effects of various visual cues in a 360 panorama-based Mixed Reality remote collaboration system. We conducted a series of user studies using a prototype system to compare the effects of different combinations of visual cues including hand gestures, ray pointing and drawing in a remote collaborative task. We found that adding ray pointing and drawing can enhance the social aspects of a remote collaborative experience and also can lower the required task mental effort. We discuss the findings and suggest directions for future research.
Research on Urine Sediment Images Recognition Based on Deep Learning

Detection of urine sediment microscopic images of human urine samples plays an important part in vitro examination. Doctors usually use automatic urine sediment analyzer to assist manual examine. At present, automatic urine sediment analyzers mostly use traditional method of artificial feature extraction to recognize urine sediment images. However, traditional image processing methods based on the selection and combination of feature operators and classifiers require a lot of work and subjective experience for engineers in the implementation process. It's also difficult to deal with urine sediment images recognition tasks with large scale categories, and particles in some different categories are often confused in recognition using traditional image processing methods, such as red blood cells (RBCs) and white blood cells (WBCs). In this paper, a combination convolution neural network (CNN) recognition method with area feature algorithm is proposed. The disadvantage that CNN can weaken the area feature of input image is solved by area feature algorithm (AFA) proposed in this paper. The network models which use 300,000 urine sediment images for training can quickly and accurately recognize 10 categories of urine sediment images, and several confusing categories' recognition indexes are remarkably improved. The test accuracy in the test set reached 97%.
Simultaneous Super-Resolution and Segmentation for Remote Sensing Images

In this paper, we present an algorithm to simultaneously obtain high-resolution images and segmentation maps from low-resolution inputs. Super-resolution and segmentation both are challenging task, but they may have certain relationship. Super-resolution will provide images with more details that may help to improve the segmentation accuracy, while label maps in segmentation dataset may contribute to finer edges during super-resolution process. Therefore, we aim to combine these two tasks and explore the influence for each other. For this end, we proposed a new deep neural network to simultaneously address the super-resolution and segmentation tasks for remote sensing images, which is named S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net. The S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net is an integrated network composed of a super-resolution sub-network and a segmentation sub-network, which is trained in an end-to-end manner. Experimental results demonstrate that this combination can enhance the performance on these two tasks.
User validation in ontology alignment: functional assessment and impact

Abstract User validation is one of the challenges facing the ontology alignment community, as there are limits to the quality of the alignments produced by automated alignment algorithms. In this paper, we present a broad study on user validation of ontology alignments that encompasses three distinct but inter-related aspects: the profile of the user, the services of the alignment system, and its user interface. We discuss key issues pertaining to the alignment validation process under each of these aspects and provide an overview of how current systems address them. Finally, we use experiments from the Interactive Matching track of the Ontology Alignment Evaluation Initiative 2015–2018 to assess the impact of errors in alignment validation, and how systems cope with them as function of their services.
Towards Effective and Interpretable Person-Job Fitting

The diversity of job requirements and the complexity of job seekers' abilities put forward higher requirements for the accuracy and interpretability of Person-Job Fit system. Interpretable Person-Job Fit system can show reasons for giving recommendations or not recommending specific jobs to some people, and vice versa. Such reasons help us understand according to what the final decision is made by the system and guarantee a high recommending accuracy. Existing studies on Person-Job Fit have focused on 1) one perspective, without considering the variances of role and psychological motivation between interviewer and job seeker; 2) modeling the matching degree between resume and job requirements directly through a deep neural network without interaction matching modules, which leads to shortage on interpretation. To this end, we propose an Interpretable Person-Job Fit (IPJF) model, which 1) models the Person-Job Fit problem from the perspectives/intentions of employer and job seeker in a multi-tasks optimization fashion to interpretively formulate the Person-Job Fit process; 2) leverages deep interactive representation learning to automatically learn the interdependence between a resume and job requirements without relying on a clear list of job seeker's abilities, and deploys the optimizing problem as a learning to rank problem. Experiments on large real dataset show that the proposed IPJF model outperforms state-of-the-art baselines and also gives promising interpretable recommending reasons.
Towards convergence of mobile and psychological theories of learning

This special issue was designed to promote an integration of mobile and psychological theories of learning by inviting empirical research that draws upon both theoretical approaches to guide investigation into learning involving mobile devices. Five empirical articles illustrated how mobile devices afford resources to learners and how new channels of data afford researchers new insight into learning processes. Authors of two invited commentaries note the challenges involved in researching mobile learning, which unfolds across multiple contexts and can involve novel tools, multiple learners, and instructors and experts. These authors propose a taxonomy that can organize research that investigates interactions amongst learners, instructors, experts, and tools across one or more physical contexts, as well as a research agenda that would empirically test and refine assumptions made by mobile learning theorists. In this commentary, the editorial team proposes that mobile and psychological theories may be improved through convergence. Theories of mobile learning can be advanced by adopting practices previously employed to refine psychological theories of learning, whereas conducting research using mobile devices (and the data they provide) can further refine psychological theories of learning. We illustrate these positions with examples, and consider how instruction must be designed and how learners must be prepared in order to benefit from learning using mobile technology.
CE-HEAT: An Aspect-Level Sentiment Classification Approach With Collaborative Extraction Hierarchical Attention Network

Aspect-level sentiment classification is a fine-grained sentiment analysis task.It aims to predict the sentiment of a review in different aspects.Recently many works exploit the hierarchical attention network to capture the aspect-specific sentiment information.However, the prior work only attends to use the aspect terms to capture the aspect-specific sentiment information in the text.It may cause the mismatch of sentiment for the aspect-specific when the aspect words are extracted incorrectly.Since the number of aspect words is much more than that sentiment words, some uncommon aspect terms are more difficult to be extracted than uncommon sentiment terms.To solve this problem, we propose a collaborative extraction hierarchical attention network.It consists of two hierarchical attention units, i.e..One unit extracts the sentiment features by the sentiment attention layer and uses the sentiment features to capture the specific aspect-related sentiment aspect information by the aspect attention layer.The other extracts the aspect features by the aspect attention layer and uses the aspect features to help capture the aspect-specific sentiment information by the sentiment attention layer.Moreover, we use the SemEval competition data set to verify our approach.The experiment shows that our approach achieves better performance than the methods which only use aspect features to extract sentiment feature for aspect-level sentiment classification.
DNN-DP: Differential Privacy Enabled Deep Neural Network Learning Framework for Sensitive Crowdsourcing Data

Deep neural network (DNN) learning has witnessed significant applications in various fields, especially for prediction and classification. Frequently, the data used for training are provided by crowdsourcing workers, and the training process may violate their privacy. A qualified prediction model should protect the data privacy in training and classification/prediction phases. To address this issue, we develop a differential privacy (DP)-enabled DNN learning framework, DNN-DP, that intentionally injects noise to the affine transformation of the input data features and provides DP protection for the crowdsourced sensitive training data. Specifically, we correspondingly estimate the importance of each feature related to target categories and follow the principle that less noise is injected into the more important feature to ensure the data utility of the model. Moreover, we design an adaptive coefficient for the added noise to accommodate the heterogeneous feature value ranges. Theoretical analysis proves that DNN-DP preserves ε-differentially private in the computation. Moreover, the simulation based on the US Census data set demonstrates the superiority of our method in predictive accuracy compared with other existing privacy-aware machine learning methods.
Red-cyan anaglyph image watermarking using DWT, Hadamard transform and singular value decomposition for copyright protection

This paper proposes a unique Red-Cyan Anaglyph image watermarking using DWT, Fast Walsh Hadamard transform and SVD. Watermarking of this particular image is not done extensively so far. Thus, this study focuses on the watermarking of red-cyan anaglyph image for copyright protection. With the help of DWT, Hadamard transform and SVD, the system tries to extract a robust feature to embed the watermark. The watermarking is carried out as. The cover image is transformed using Discrete Wavelet Transform and the watermark image of size 32 × 32 is scrambled using Arnold transform and it is hidden into the hadamard transformed blocks of the Anaglyph image exploiting singular values obtained by applying Singular Value Decomposition. The performance of this proposed system is checked for its robust nature and its imperceptibility on Middlebury Stereo database against usual image processing attacks such as Gaussian noise, filtering, etc. From the simulation results, it is clearly known that it has achieved better outcomes in terms of robustness and imperceptibility than the former existing papers.
Mixtures of skewed matrix variate bilinear factor analyzers

In recent years, data have become increasingly higher dimensional and, therefore, an increased need has arisen for dimension reduction techniques for clustering. Although such techniques are firmly established in the literature for multivariate data, there is a relative paucity in the area of matrix variate, or three-way, data. Furthermore, the few methods that are available all assume matrix variate normality, which is not always sensible if cluster skewness or excess kurtosis is present. Mixtures of bilinear factor analyzers using skewed matrix variate distributions are proposed. In all, four such mixture models are presented, based on matrix variate skew-t, generalized hyperbolic, variance-gamma, and normal inverse Gaussian distributions, respectively.
Teleportation-based quantum homomorphic encryption scheme with quasi-compactness and perfect security

Quantum homomorphic encryption (QHE) is an important cryptographic technology for delegated quantum computation. It enables remote server to perform quantum computation on encrypted data, and the specific algorithm performed by Server is unnecessarily known by Client. Quantum fully homomorphic encryption (QFHE) is a QHE that satisfies both compactness and $${\mathcal {F}}$$-homomorphism (homomorphic for any quantum circuits). However, Yu et al. (Phys Rev A 90:050303, 2014) proved a negative result: Assume interaction is not allowed, it is impossible to construct perfectly secure QFHE scheme. So this article focuses on non-interactive and perfectly secure QHE scheme with loose requirement, e.g., quasi-compactness. This article defines encrypted gate, which is denoted by $$EG[U]:|\alpha \rangle \rightarrow \left( (a,b),Enc_{a,b}(U|\alpha \rangle )\right) $$. We present a gate-teleportation-based two-party computation scheme for EG[U], where one party gives arbitrary quantum state $$|\alpha \rangle $$ as input and obtains the encrypted U-computing result $$Enc_{a,b}(U|\alpha \rangle )$$, and the other party obtains the random bits a, b. Based on $$EG[P^x](x\in \{0,1\})$$, we propose a method to remove the P-error generated in the homomorphic evaluation of $$T/T^\dagger $$-gate. Using this method, we design two non-interactive and perfectly secure QHE schemes named GT and VGT. Both of them are $${\mathcal {F}}$$-homomorphic and quasi-compact (the decryption complexity depends on the $$T/T^\dagger $$-gate complexity). Assume $${\mathcal {F}}$$-homomorphism, non-interaction and perfect security are necessary properties, the quasi-compactness is proved to be bounded by $$\varOmega (M)$$, where M is the total number of $$T/T^\dagger $$-gates in the evaluated circuit. We prove VGT is M-quasi-compact and reaches the optimal bound. According to our QHE schemes, the decryption would be inefficient when the evaluated circuit contains exponential number of $$T/T^\dagger $$-gates. Thus, our schemes are suitable for homomorphic evaluation of any quantum circuit with low $$T/T^\dagger $$-gate complexity, such as any polynomial-size quantum circuit or any quantum circuit with polynomial number of $$T/T^\dagger $$-gates.