Reviewerly/exHarmony huggingface dataset:
Biometrics-Based Un-Locker to Enhance Cloud Security Systems

With the increasing demand for cloud computing, its challenges are also increasing, and security is one of the preeminent concerns. A number of approaches were introduced earlier to secure cloud storage systems, but many of them failed to provide security effectively and even emanate the concern of cost. Efficiency and cost are the essential features for any cloud system and demand for a security mechanism. To accomplish this objective, a novel feature is introduced with a concept of biometric authentication and named as ‘biometric-based un-locker'. Biometrics entities are unique in nature, so it will be the best and most cost-effective solution for the cloud. The performance of this proposed system is analyzed by generating a cloud environment and implementing it using the .NET framework on five different datasets. The results are calculated with both attackers and normal users, and performance is analyzed on the basis of time, size of data after encryption, and authentication rate. Results show that the proposed system effectively provides security and is also very cost-efficient.
An Improved Hybrid Whale Optimization Algorithm Based on Differential Evolution

The standard whale algorithm is easy to fall into the local optimum, in order to further improve the performance of the whale optimization algorithm, improve the local search ability and global search ability of the algorithm, a new hybrid whale optimization algorithm is proposed in this paper. Differential evolution algorithm is combined with the standard whale optimization algorithm, and chaos initialization population and chaos disturbance, adaptive convergence operator, adaptive selection and other operations are added to the algorithm. Several test functions have been used to prove its superiority in accuracy and speed.
A Fast Perceptual Surveillance Video Coding (PSVC) Based on Background Model-Driven JND Estimation

Perceptual video coding (PVC) optimization has been an important video coding technique, which can be consistent with the perception characteristics of the human visual system (HVS). Currently, PVC schemes incorporating the just noticeable distortion (JND) model can obtain better performance gain in all PVC schemes. To further accelerate the JND computation for real-time video coding applications (e.g. surveillance video coding and conference video coding), this paper proposes a fast perceptual surveillance video coding (PSVC) scheme based on background model-driven JND estimation method. First, to utilize the surveillance scene characteristics, the computation complexity of JND estimation can be significantly decreased by reusing the content complexity of background regions. Then we apply the perceptive video coding scheme into the background modeling-based surveillance video codec. The proposed scheme adopts background modeling frame as background anchor. Experimental results show that the proposed scheme can yield remarkable time saving of 42.33% maximum and on average 34.76% with approximate bitrate reductions and similar subjective quality, compared to HEVC and other state-of-the-art schemes.
La Stanza nella Teoria della Lingua in Atto

This paper presents a corpus-based analysis of Italian spontaneous speech in the theoretical framework of Language into Act Theory (Cresti 2000; Cresti &amp; Moneglia 2010; Moneglia &amp; Raso 2014). A selection of IPIC database (Panunzi &amp; Gregori 2012) has been studied to describe the syntactic behaviors of specific structures named stanzas. Stanzas are terminated sequences prosodically recognizable in the speech flow as chains of nuclei on the same prosodic level. This structure marks nearly 10% of the spoken communication and it is typical of monologues, stories, and life narrations. The analysis shows an additive and flat syntax mostly composed by chained clauses and juxtapositions, without a hierarchical order: stanzas are usually formed by main clauses one after the other or connected through coordinative conjunctions; subordination is not so common and mostly in the form of causal or relative subordinate clauses.
Obstacle Avoidance Algorithm for Redundant Manipulators Based on Pseudo-distance Method

Focusing on the obstacle avoidance problem of redundant manipulators, an algorithm based on pseudo-distance technique for avoiding obstacles in redundant robot control is presented without master-slaver task conversion.The proximity between the redundant manipulator and the obstacle is calculated by using the pseudo-distance algorithm with analytic form.Then the minimum Euclidean distance is obtained by analyzing the conversion relation of the pseudo-distance and Euclidean distance, leading to resolving the minimum Euclidean distance efficiently between the manipulator and the obstacle.On the one part, based on the identification index of minimum pseudo-distance, obstacle avoidance at end-effector of manipulator is accomplished by endowing a repelling velocity of end-effector along the direction of minimum Euclidean distance.On the other hand, an adaptive damping control method based on the identification index is proposed for obstacle avoidance in null space of a redundant robot, generating repelling virtual force to push robot links away from obstacles and adjusting damping factors adaptively responding to repelling force.Finally, the efficiency and effectiveness of the proposed algorithm is verified by simulations and experiments of a 7 Degree-of-Freedom (DOF) redundant manipulator.The simulation results show the oscillation of end-effector caused by the master-slave task transformation of the traditional obstacle-avoiding method is avoided and obstacle avoidance running time is reduced dramatically in the end-effector obstacle avoidance, and in the null space obstacle avoidance, the avoidance of collisions between the manipulator and the obstacle is realized successfully under the low error tracking the desired trajectory.The final experiment results show that the multiple obstacles
Looking into international research groups’ digital discursive practices: Criteria and methodological steps taken towards the compilation of the EUROPRO digital corpus

The EUROPRO digital corpus was designed by the InterGedi research group, based at the University of Zaragoza (Spain). The main focus of InterGedi is the analysis of the textual resources used by international research groups as part of their dissemination and visibility strategies. The corpus comprises a collection of 30 international research project websites funded by the European Horizon2020 Programme (EUROPROwebs corpus). By looking into their websites, 20 projects were observed to maintain a Twitter account and the tweets from these accounts were the basis for the compilation of the EUROPROtweets corpus. This paper delves into the criteria used for the selection of the research project websites and the methodological steps taken to classify, label and tag the verbal component in these websites and tweets. The paper discusses the challenges in the compilation of the corpus because of the dynamic, hypermodal, and hypermedial nature of the digital texts it contains. The paper closes by underlining the potential uses and applications of EUROPRO in order to gain insights into the digital discursive and professional practices used by international research groups to foster their visibility online.
Image Inpainting Using Two-Stage Loss Function and Global and Local Markovian Discriminators

Image inpainting networks can produce visually reasonable results in the damaged regions. However, existing inpainting networks may fail to reconstruct the proper structures or tend to generate the results with color discrepancy. To solve this issue, this paper proposes an image inpainting approach using the proposed two-stage loss function. The loss function consists of different Gaussian kernels, which are utilized in different stages of network. The use of our two-stage loss function in coarse network helps to focus on the image structure, while the use of it in refinement network is helpful to restore the image details. Moreover, we proposed a global and local PatchGANs (GAN means generative adversarial network), named GL-PatchGANs, in which the global and local markovian discriminators were used to control the final results. This is beneficial to focus on the regions of interest (ROI) on different scales and tends to produce more realistic structural and textural details. We trained our network on three popular datasets on image inpainting separately, both Peak Signal to Noise ratio (PSNR) and Structural Similarity (SSIM) between our results, and ground truths on test images show that our network can achieve better performance compared with the recent works in most cases. Besides, the visual results on three datasets also show that our network can produce visual plausible results compared with the recent works.
Tell Me Why You Feel That Way: Processing Compositional Dependency for Tree-LSTM Aspect Sentiment Triplet Extraction (TASTE)

Sentiment analysis has transitioned from classifying the sentiment of an entire sentence to providing the contextual information of what targets exist in a sentence, what sentiment the individual targets have, and what the causal words responsible for that sentiment are. However, this has led to elaborate requirements being placed on the datasets needed to train neural networks on the joint triplet task of determining an entity, its sentiment, and the causal words for that sentiment. Requiring this kind of data for training systems is problematic, as they suffer from stacking subjective annotations and domain over-fitting leading to poor model generalisation when applied in new contexts. These problems are also likely to be compounded as we attempt to jointly determine additional contextual elements in the future. To mitigate these problems, we present a hybrid neural-symbolic method utilising a Dependency Tree-LSTM's compositional sentiment parse structure and complementary symbolic rules to correctly extract target-sentiment-cause triplets from sentences without the need for triplet training data. We show that this method has the potential to perform in line with state-of-the-art approaches while also simplifying the data required and providing a degree of interpretability through the Tree-LSTM.
An adaptive total variational despeckling model based on gray level indicator frame

&lt;p style='text-indent:20px;'&gt;For the characteristics of the degraded images with multiplicative noise, the gray level indicators for constructing adaptive total variation are proposed. Based on the new regularization term, we propose the new convex adaptive variational model. Then, considering the existence, uniqueness and comparison principle of the minimizer of the functional. The finite difference method with rescaling technique and the primal-dual method with adaptive step size are used to solve the minimization problem. The paper ends with a report on numerical tests for the denoising of images subject to multiplicative noise, the comparison with other methods is provided as well.&lt;/p&gt;
Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions

Translating verbose information needs into crisp search queries is a phenomenon that is ubiquitous but hardly understood. Insights into this process could be valuable in several applications, including synthesizing large privacy-friendly query logs from public Web sources which are readily available to the academic research community. In this work, we take a step towards understanding query formulation by tapping into the rich potential of community question answering (CQA) forums. Specifically, we sample natural language (NL) questions spanning diverse themes from the Stack Exchange platform, and conduct a large-scale conversion experiment where crowdworkers submit search queries they would use when looking for equivalent information. We provide a careful analysis of this data, accounting for possible sources of bias during conversion, along with insights into user-specific linguistic patterns and search behaviors. We release a dataset of 7,000 question-query pairs from this study to facilitate further research on query understanding.
Utilizing Multimodal Feature Consistency to Detect Adversarial Examples on Clinical Summaries

Recent studies have shown that adversarial examples can be generated by applying small perturbations to the inputs such that the welltrained deep learning models will misclassify.With the increasing number of safety and security-sensitive applications of deep learning models, the robustness of deep learning models has become a crucial topic.The robustness of deep learning models for healthcare applications is especially critical because the unique characteristics and the high financial interests of the medical domain make it more sensitive to adversarial attacks.Among the modalities of medical data, the clinical summaries have higher risks to be attacked because they are generated by third-party companies.As few works studied adversarial threats on clinical summaries, in this work we first apply adversarial attack to clinical summaries of electronic health records (EHR) to show the text-based deep learning systems are vulnerable to adversarial examples.Secondly, benefiting from the multi-modality of the EHR dataset, we propose a novel defense method, MATCH (Multimodal feATure Consistency cHeck), which leverages the consistency between multiple modalities in the data to defend against adversarial examples on a single modality.Our experiments demonstrate the effectiveness of MATCH on a hospital readmission prediction task comparing with baseline methods.
Qubit-Qutrit (2 ⊗ 3) Quantum Systems: an Investigation of Some Quantum Correlations Under Collective Dephasing

We revisit qubit-qutrit quantum systems under collective dephasing and answer some of the questions which have not been asked and addressed so far in the literature. In particular, we examine the possibilities of non-trivial phenomena of {\it time-invariant} entanglement and {\it freezing} dynamics of entanglement for this dimension of Hilbert space. Interestingly, we find that for qubit-qutrit systems both of these peculiar features coexist, that is, we observe not only time-invariant entanglement for certain quantum states but we find also find evidence that many quantum states freeze their entanglement after decaying for some time. To our knowledge, the existance of both these phenomena for one dimension of Hilbert space is not found so far. All previous studies suggest that if there is freezing dynamics of entanglement, then there is no time-invariant entanglement and vice versa. In addition, we study local quantum uncertainity and other correlations for certain families of states and discuss the interesting dynamics. Our study is an extension of similar studies for qubit-qubit systems, qubit-qutrit, and multipartite quantum systems.
A Visually-grounded First-person Dialogue Dataset with Verbal and Non-verbal Responses

In real-world dialogue, first-person visual information about where the other speakers are and what they are paying attention to is crucial to understand their intentions.Non-verbal responses also play an important role in social interactions.In this paper, we propose a visuallygrounded first-person dialogue (VFD) dataset with verbal and non-verbal responses.The VFD dataset provides manually annotated (1) first-person images of agents, (2) utterances of human speakers, (3) eye-gaze locations of the speakers, and (4) the agents' verbal and nonverbal responses.We present experimental results obtained using the proposed VFD dataset and recent neural network models (e.g., BERT, ResNet).The results demonstrate that firstperson vision helps neural network models correctly understand human intentions, and the production of non-verbal responses is a challenging task like that of verbal responses.Our dataset is publicly available 1 .1
The resource theory of coherence for quantum channels

We define the quantum-incoherent relative entropy of coherence ( $$\mathcal {QI}$$ REC) of quantum channels in the framework of the resource theory by using the Choi–Jamiolkowski isomorphism. Coherence-breaking channels are introduced as free operations and their corresponding Choi states as free states. We also show the relationship between the coherence of channel and the quantum discord and find that basis-dependent quantum asymmetric discord can never be more than the $$\mathcal {QI}$$ REC for any quantum channels. Also, we prove the $$\mathcal {QI}$$ REC is decreasing for any divisible quantum-incoherent channel and we also claim it can be considered as the quantumness of quantum channels. Moreover, we demonstrate that for qubit channels, the relative entropy of coherence (REC) can be equivalent to the REC of their corresponding Choi states and the basis-dependent quantum symmetric discord can never exceed the coherence.
Random number generation and distribution out of thin (or thick) air

Much scientific work has focused on the generation of random numbers as well as the distribution of said random numbers for use as a cryptographic key. However, emphasis is often placed on one of the two to the exclusion of the other, but both are often simultaneously important. Here we present a simple hybrid free-space link scheme for both the generation and secure distribution of (pseudo-)random numbers between two remote parties, drawing the randomness from the stochastic nature of atmospheric turbulence. The atmosphere is simulated using digital micro-mirror devices for efficient, all-digital control. After outlining one potential algorithm for extracting random numbers based on finding the centre-of-mass (COM) of turbulent beam intensity profiles, the statistics of our experimental COM measurements is studied and found to agree well with the literature. After implementing the scheme in the laboratory, Alice and Bob are able to establish a string of correlated random bits with an 84% fidelity. Finally, we make a simple modification to the original setup in an attempt to thwart the hacking attempts of an eavesdropper, Eve, who has access to the free-space portion of the link. We find that the fidelity between Eve's key and that of Alice/Bob is 54%, only slightly above the theoretical minimum. Atmospheric turbulence could hence be leveraged as an added security measure, rather than being seen as a drawback.
Action principle and weak invariants

A weak invariant associated with a master equation is characterized in such a way that its spectrum is not constant in time but its expectation value is conserved under time evolution generated by the master equation. Here, an intriguing relationship between the concept of weak invariants and the action principle for master equations based on the auxiliary operator formalism is revealed. It is shown that the auxiliary operator can be thought of as a weak invariant.
MEIM: A Multi-source Software Knowledge Entity Extraction Integration Model

Entity recognition and extraction are the foundations of knowledge graph construction.Entity data in the field of software engineering come from different platforms and communities, and have different formats.This paper divides multi-source software knowledge entities into unstructured data, semi-structured data and code data.For these different types of data, Bi-directional Long Short-Term Memory (Bi-LSTM) with Conditional Random Field (CRF), template matching, and abstract syntax tree are used and integrated into a multi-source software knowledge entity extraction integration model (MEIM) to extract software entities.The model can be updated continuously based on user's feedbacks to improve the accuracy.To deal with the shortage of entity annotation datasets, keyword extraction methods based on Term Frequency-Inverse Document Frequency (TF-IDF), TextRank, and K-Means are applied to annotate tasks.The proposed MEIM model is applied to the Spring Boot framework, which demonstrates good adaptability.The extracted entities are used to construct a knowledge graph, which is applied to association retrieval and association visualization.
Who can spot an online romance scam?

Purpose This paper aims to examine predictors (personality, belief systems, expertise and response time) of detecting online romance scams. Design/methodology/approach The online study asked 261 participants to rate whether a profile was a scam or a genuine profile. Participants were also asked to complete a personality inventory, belief scales and demographic, descriptive questions. The online study was also designed to measure response time. Findings It was found that those who scored low in romantic beliefs, high in impulsivity, high in consideration of future consequences, had previously spotted a romance scam and took longer response times were more likely to accurately distinguish scams from genuine profiles. Notably, the research also found that it was difficult to detect scams. The research also found that it was important to adapt Whitty’s (2013) “Scammers Persuasive Techniques Model” to include a stage named: “human detection of scam versus genuine profiles”. Originality/value This is the first study, to the author’s knowledge, that examines predictors of human accuracy in detecting romance scams. Dating sites and government e-safety sites might draw upon these findings to help improve human detection and protect users from this financial and psychologically harmful cyberscam.
Compact quadratizations for pseudo-Boolean functions

The problem of minimizing a pseudo-Boolean function, that is, a real-valued function of 0–1 variables, arises in many applications. A quadratization is a reformulation of this nonlinear problem into a quadratic one, obtained by introducing a set of auxiliary binary variables. A desirable property for a quadratization is to introduce a small number of auxiliary variables. We present upper and lower bounds on the number of auxiliary variables required to define a quadratization for several classes of specially structured functions, such as functions with many zeros, symmetric, exact k-out-of-n, at least k-out-of-n and parity functions, and monomials with a positive coefficient, also called positive monomials. Most of these bounds are logarithmic in the number of original variables, and we prove that they are best possible for several of the classes under consideration. For positive monomials and for some other symmetric functions, a logarithmic bound represents a significant improvement with respect to the best bounds previously published, which are linear in the number of original variables. Moreover, the case of positive monomials is particularly interesting: indeed, when a pseudo-Boolean function is represented by its unique multilinear polynomial expression, a quadratization can be obtained by separately quadratizing its monomials.
A Noise-Acceptable ZNN for Computing Complex-Valued Time-Dependent Matrix Pseudoinverse

The issue of complex-valued time-dependent pseudoinverse often exists in science and engineering fields.In the existing studies, many models were presented for solving complex-valued timedependent pseudoinverse in the noiseless environments.However, the appearance of noise is unavoidable in practice.In this paper, a novel noise-acceptable zeroing neural network (NAZNN) model is first proposed for computing complex-valued time-dependent matrix pseudoinverse with different noise situations.For comparison, the traditional zeroing neural network and the gradient neural network are adopted to complete the same task.Theoretical analyses prove that the proposed NAZNN model obtains the global exponential convergence performance.Moreover, the proposed NAZNN is also proven to obtain strong resistance to various sorts of noise.Finally, the results of numerical experiments further substantiate the theoretical analysis and indicate the effectiveness and superiority of the proposed NAZNN model for computing complex-valued time-dependent matrix pseudoinverse in various kinds of noise.