Reviewerly/exHarmony huggingface dataset:
Digital image encryption scheme based on generalized Mandelbrot-Julia set

With the rapid development of quantum computer, the encryption schemes in low key space are facing a huge security threat. The chaotic-based image encryption schemes use the initial values of the systems as the key, so the key spaces are often unchanged, which provide a way for the cracker to perform an exhaustive attack. This paper proposes an image encryption scheme based on generalized Mandelbrot-Julia (M-J) set. The key space of the scheme is not only large, but also dynamically changeable. To verify the feasibility of the generalized M-J set in the encryption scheme we use the simplest two rounds XOR encryption algorithm. We get the key by using environmental noise and SHA-512. The use of one-time keys not only improves the security of the algorithm, but also weakens the difficulty of transmitting many secret keys. Simulation results show that the key space of the our scheme can reach to 23250, which is far more than the most schemes. In order to verify the security of the encryption scheme, we have made corresponding security analysis for many attack methods. The results show that all the performance indexes satisfy the image encryption secure performance test standards. Finally, we tested the randomness of the generalized M-J set using the NIST randomness test software. Various analyses show that the encryption scheme is safe, and the generalized M-J set is an excellent system that can be applied in cryptography.
An LSTM-CNN attention approach for aspect-level sentiment classification

Opinions in complex reviews often vary on different aspects of a thing. Coarse-grained sentiment analysis on a sentence can’t capture the sentiment polarity of it accurately. Therefore, aspect-level sentiment classification is a better choice because it is a fine-grained task in sentiment analysis. However, common methods used for sentiment classification are not suitable for fine-grained sentiment analysis, such as LSTM. LSTM works in a sequential way and manipulates each context word with the same operation, so that it cannot explicitly reveal the importance of each context word. To this end, we propose an Aspect-based LSTM-CNN Attention model for aspect-level sentiment classification. Our approach combines LSTM with CNN for simultaneously leveraging LSTM to handle long-range dependencies and CNN’s ability to identify local features. The features extracted by LSTM will be filtered again by convolution and pooling operations to find important local features. Then we introduce the attention mechanism to focus on the important information about the specific aspect. We experiment on the SemEval 2014 dataset and Chinese hotel reviews, and results show that our model can effectively improve the accuracy of aspect-level sentiment classification.
Experimental preparation of Greenberger-Horne-Zeilinger states in an Ising spin model by partially suppressing the nonadiabatic transitions

The creation of multipartite entangled states is a key task of quantum information processing. Among the various implementations, shortcut to adiabaticity (STA) offers a fast and robust means for generating entanglement. The traditional counterdiabatic driving, as a conventional and simple method for STA, suppresses transitions with an auxiliary Hamiltonian, but its complex interactions in many-body systems may hamper the feasibility of experimental implementation. To avoid this drawback, a flexible and efficient way was proposed theoretically by Chen et al. [Phys. Rev. A 93, 052109 (2016)] by substituting the counterdiabatic terms. Inspired by this work, we devise a practical protocol for preparing the Greenberger-Horne-Zeilinger state on the Ising spin model via STA by partial suppression of the nonadiabatic transitions, which can obviously reduce the complexity in experiments compared with the original method. We also experimentally demonstrate the viability of our scheme with a nuclear magnetic resonance quantum simulator. This work provides an alternative method to realize fast coherent quantum control for a multiqubit system in experiments.
A Hierarchical Sorting Swarm Optimizer for Large-Scale Optimization

Large-scale optimization is a challenging problem because it involves a large number of decision variables. In this paper, a simple but effective method, called hierarchical sorting swarm optimizer (HSSO), is proposed for large-scale optimization. As a variant of representative particle swarm optimizer (PSO), HSSO first sorts the initial particles according to their fitness values, and then partitions the sorted particles into two groups, namely, the good group corresponding to better fitness values, and the bad group with worse fitness values. The bad group is then updated by learning from the good one. After that, we take the good group as a new swarm and conduct the sorting and learning procedures. The aforementioned operations are repeated several times until only one particle left to form a hierarchical structure. In the experiments, HSSO is applied to optimize 39 benchmark test functions. The comparative results with several existing algorithms demonstrate that, despite its simplicity, HSSO shows improved performance in terms of both exploration and exploitation.
Nominal licensing is driven by valued (phi-)features

This short paper lays out the components of a new model of nominal licensing, motivated by novel observations about parallels between the Person Case Constraint and Differential Object Marking. The model revolves around the idea that valued features on nominals---namely, phi-features and features related to definiteness and animacy---are the sorts of features that need abstract licensing, rather than an abstract Case feature. This model helps us understand where differential marking and featural restrictions occur, and in particular, why it is that subjects and indirect objects, in contrast to direct objects, tend not to be differentially marked or featurally restricted.
Talk2Me: Automated linguistic data collection for personal assessment

Language is one the earliest capacities affected by cognitive change. To monitor that change longitudinally, we have developed a web portal for remote linguistic data acquisition, called Talk2Me, consisting of a variety of tasks. In order to facilitate research in different aspects of language, we provide baselines including the relations between different scoring functions within and across tasks. These data can be used to augment studies that require a normative model; for example, we provide baseline classification results in identifying dementia. These data are released publicly along with a comprehensive open-source package for extracting approximately two thousand lexico-syntactic, acoustic, and semantic features. This package can be applied arbitrarily to studies that include linguistic data. To our knowledge, this is the most comprehensive publicly available software for extracting linguistic features. The software includes scoring functions for different tasks.
The National Security Implications of Cyberbiosecurity

The cyber- and biological sciences are converging rapidly, creating benefits, new and advantageous applications, and increasing risks to all nations. The parts of the public and private sectors that should be responsible for cyberbiosecurity are not yet sufficiently organized or supported financially. This article addresses the need to ensure that national security policy: (1) assesses cyberbiological risk and incorporates deterrent and enforcement measures; (2) sets forth clear consequences for those individuals and countries that conduct cyberbiological attacks or otherwise compromise cyberbiosecurity, without imperiling the legitimate sharing of scientific data and information; (3) establishes voluntary cyberbiosecurity standards in partnership with the private sector; (4) identifies cyberbiosecurity threats, vulnerabilities, consequences, and solutions; and (5) results from the combined efforts of all branches of government and the private sector.
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Let Na be the number of solutions to the equation x2k+1+x+a=0 in F2n where gcd⁡(k,n)=1. In 2004, by Bluher [2] it was known that possible values of Na are only 0, 1 and 3. In 2008, Helleseth and Kholosha [13] found criteria for Na=1 and an explicit expression of the unique solution when gcd⁡(k,n)=1. In 2010 [14], the extended version of [13], they also got criteria for Na=0,3. In 2014, Bracken, Tan and Tan [5] presented another criterion for Na=0 when n is even and gcd⁡(k,n)=1. This paper completely solves this equation x2k+1+x+a=0 with only the condition gcd⁡(n,k)=1. We explicitly calculate all possible zeros in F2n of Pa(x). New criteria for which a, Na is equal to 0, 1 or 3 are by-products of our result.
Fly with the flock: immersive solutions for animal movement visualization and analytics

Understanding the movement of animals is important for a wide range of scientific interests including migration, disease spread, collective movement behaviour and analysing motion in relation to dynamic changes of the environment such as wind and thermal lifts. Particularly, the three-dimensional (3D) spatial-temporal nature of bird movement data, which is widely available with high temporal and spatial resolution at large volumes, presents a natural option to explore the potential of immersive analytics (IA). We investigate the requirements and benefits of a wide range of immersive environments for explorative visualization and analytics of 3D movement data, in particular regarding design considerations for such 3D immersive environments, and present prototypes for IA solutions. Tailored to biologists studying bird movement data, the immersive solutions enable geo-locational time-series data to be investigated interactively, thus enabling experts to visually explore interesting angles of a flock and its behaviour in the context of the environment. The 3D virtual world presents the audience with engaging and interactive content, allowing users to 'fly with the flock', with the potential to ascertain an intuitive overview of often complex datasets, and to provide the opportunity thereby to formulate and at least qualitatively assess hypotheses. This work also contributes to ongoing research efforts to promote better understanding of bird migration and the associated environmental factors at the global scale, thereby providing a visual vehicle for driving public awareness of environmental issues and bird migration patterns.
Characterizing the Biomedical Data-Sharing Landscape

Advances in technologies and biomedical informatics have expanded capacity to generate and share biomedical data. With a lens on genomic data, we present a typology characterizing the data-sharing landscape in biomedical research to advance understanding of the key stakeholders and existing data-sharing practices. The typology highlights the diversity of data-sharing efforts and facilitators and reveals how novel data-sharing efforts are challenging existing norms regarding the role of individuals whom the data describe.
Privacy preserving similarity joins using MapReduce

Similarity join is an essential operator in data processing, mining and analysis. However, it is resource intensive and time consuming, particularly when processing big data. There is also a need to ensure data confidentiality in similarity joins, as joining between two files may result in personal information disclosure. Based on these two considerations, this paper proposes a MapReduce-based similarity joins with differential privacy technology (hereafter, referred to as PSJoin). The proposed parallel algorithm is designed to achieve high efficiency, in terms of answering similarity join queries privately and effectively. Specifically, the use of PSJoin ensures the preservation of privacy during the similarity join process and in the published results. A new private global ordering approach is presented in this paper, which is designed to deal with potential disclosure issues during the process, and a differential private similarity function is provided for this algorithm. Findings from our evaluations using large-scale real-world datasets demonstrate that our method can effectively guarantee privacy with only minimal accuracy loss in similarity queries, while offering good scalability consistently.
Automatic design of semantic similarity controllers based on fuzzy logics

Recent advances in machine learning have been able to make improvements over the state-of-the-art regarding semantic similarity measurement techniques. In fact, we have all seen how classical techniques have given way to promising neural techniques. Nonetheless, these new techniques have a weak point: they are hardly interpretable. For this reason, we have oriented our research towards the design of strategies being able to be accurate enough but without sacrificing their interpretability. As a result, we have obtained a strategy for the automatic design of semantic similarity controllers based on fuzzy logics, which are automatically identified using genetic algorithms (GAs). After an exhaustive evaluation using a number of well-known benchmark datasets, we can conclude that our strategy fulfills both expectations: it is able of achieving reasonably good results, and at the same time, it can offer high degrees of interpretability.
A Study on WeChat-Based Collaborative Learning in College English Writing

With the rapid development of mobile information technology and social media networks, it is feasible for college English teachers to get access to social networks such as QQ, Email and MSN as a way of practicing English writing beyond classroom. Similarly, it is also possible for teachers to utilize WeChat Platform where online communities for students and teachers can be established to combine collaborative and mobile learning together as a complementary way of classroom writing teaching. WeChat Platform, as the most popular software in China, owns the advantages of transmitting instant message, videos and pictures, which supplies students more chances to collaborate and interact with each other/one another at different stages of writing tasks. This research explores the application collaborative learning of college English writing on WeChat Platform. Based on the one-semester research as well as the questionnaire of the pre-test and post-test, it is revealed that, although there are still a few challenges for students and teachers to face, this mode of college English writing contributes to cultivating the students&amp;rsquo; team spirit, enhancing their initiative, improving their writing efficiency and developing their critical thinking by engaging in student-student and student-teacher collaboration and interaction, information sharing, communicating and socializing with classmates.
Invertibility-Driven Interpolation Filter for Video Coding

Motion compensation with fractional motion vector has been widely utilized in the video coding standards. The fractional samples are usually generated by fractional interpolation filters. Traditional interpolation filters are usually designed based on the signal processing theory with the assumption of band-limited signal, which cannot effectively capture the non-stationary property of video content and cannot adapt to the variety of video quality. In this paper, we reveal an intuitive property of the fractional interpolation problem, named invertibility. That is, the fractional interpolation filters should not only generate fractional samples from integer samples but also recover the integer samples from the fractional samples in an invertible manner. We prove in theory that the invertibility in the spatial domain is equivalent to the constant magnitude in the Fourier transform domain. Driven by the invertibility, we then develop a learning-based method to solve the fractional interpolation problem. Inspired by the advances of convolutional neural network (CNN), we propose to establish an end-to-end scheme using CNN to train invertibility-driven interpolation filter (InvIF). Different from the previous learning-based methods, the proposed training scheme does not need hand-crafted “ground truth” of fractional samples. The proposed InvIF is integrated into high efficiency video coding (HEVC), and extensive experiments are conducted to verify its effectiveness. The experimental results show that the proposed method can achieve on average 4.7% and 3.6% BD-rate reduction compared with the HEVC anchor, under low-delay-B and random-access configurations, respectively.
Resilient Quantum Key Distribution (QKD)-Integrated Optical Networks With Secret-Key Recovery Strategy

Quantum key distribution (QKD) promises to deliver secure keys, which can be applied for security demands in optical networks by using cost-efficient and scalable lightpaths. To achieve such secure communication, the QKD integrated with optical networks has become a promising scenario to provide key provisioning services in optical networks. As an inevitable problem, the occurrence of failures becomes a challenge for the resiliency of the network. In that context, this paper studies the resilient QKD-integrated optical networks against single link failure. By analyzing and quantifying the key provisioning services, we constructed the secret-key flow model (SKFM) for the failure-affected and failure-unaffected cases. Based on the SKFM, a secret-key recovery strategy (SKRS) including three algorithms (i.e., one-path recovery method (OPRM), multi-path recovery method (MPRM), and time window-based recovery method (TWRM)) is designed to recover failure-affected key provisioning services in the network. The simulation work has been conducted to evaluate the performance of OPRM, MPRM, and TWRM in terms of key-service recovery ratio, secret-key recovery ratio, wavelength consumption ratio, and secret-key consumption ratio. Numerical results show that the three algorithms can recover failure-affected key provisioning services effectively, i.e., the MPRM outperforms the OPRM and the TWRM outperforms the MPRM. Better recovery can be realized by sacrificing more wavelength and secret-key resources, which are also required for the delivery of the QKD in the network. Thus, a trade-off can be achieved between the recovery of key provisioning services and the delivery of the QKD on wavelength resources and secret-key resources.
The Effect of Three Dimensional Virtual Environments and Augmented Reality Applications on The Learning Achievement: A Meta-Analysis Study

The objective of the study is to analyze the effect of three dimensional (3D) virtual environment and augmented reality applications on learning achievement. In line with this purpose, experimental studies were considered and the effects of experimental and control groups on learning achievement analyzed. In the experimental group, the applications which were established through the 3D virtual environment and augmented reality technologies while control group consisted of face to face environment. In order to realize this aim, the meta-analysis method used in educational sciences was preferred. Key words for 3D virtual environments meta-analysis scanning are: '"3D virtual world" & achievement' and '"3D virtual environment" & achievement'. And the key word determined for the augmented reality meta- analysis scanning is '"augmented reality" & achievement'. Within the context of the research, the databases such as Science Direct, ERIC, Taylor & Francis, EBSCO, Emerald, JSTOR, SAGE, SpringerLink and Google Scholar were analyzed. The determined keywords were entered into the aforementioned databases and 4.682 articles published between 2010 and 2016 were controlled in terms of their conformity to the objectives of the research. After the initial analysis, 47 articles were determined on 3D virtual environment while 57 articles were on the augmented reality. Among the 47 articles on 3D virtual environments, 20 articles which conform to the inclusion criteria were determined and 24 articles among the 57 articles on augmented reality were employed for meta-analysis. The dependent variable of the research is learning achievement while independent variable is experimental and control groups. Education level was determined as the moderator variable in the research. According to the results of the research, it was found that 3D virtual environments had moderate levels of effect on learning achievement on behalf of the experimental group (d=0.32). similar to those results, augmented reality applications had also moderate level of effect on learning achievement on behalf of experimental group (d=0.46). According to the results of the moderator analysis conducted, it was determined that the selection of the study sample as undergraduate, primary school, high school and secondary school did not change the effect size of the 3D virtual environments on the learning success. Again, according to the results of the moderator analysis, it was determined that the selection of the study sample as a secondary school, primary school and graduate did not change the effect size of the augmented reality applications on the learning success.
A SKELETON FEATURES-BASED FALL DETECTION USING MICROSOFT KINECT V2 WITH ONE CLASS-CLASSIFIER OUTLIER REMOVAL

Abstract. The real-time and robust fall detection is one of the key components of elderly people care and monitoring systems. Depth sensors, as they became more available, occupy an increasing place in event recognition systems. Some of them can directly produce a skeletal description of the human figure for compact representation of a person’s posture. Skeleton description makes the output of source video or detailed information about the depth outside the system unnecessary and raises the privacy of the entire system. Based on a comparative study of different RGB-D cameras, the most promising model for further development was chosen - Microsoft Kinect v2. The TST Fall Detection Dataset v2 is used here as a base for experiments. The proposed algorithm is based on the skeleton features encoding on the sequence of neighboring frames and support vector machine classifier. A version of a cumulative sum method is applied for combining the individual decisions on the consecutive frames. It is offered to use the one-class classifier for detection of low-quality skeletons. The 0.958 accuracy of our fall detection procedure was obtained in the cross-validation procedure based on the removal of records of a particular person from the database (Leave-one-Person-out).
Minimal energy cost of entanglement extraction

We compute the minimal energy cost for extracting entanglement from the ground state of a bosonic or fermionic quadratic system. Specifically, we find the minimal energy increase in the system resulting from replacing an entangled pair of modes, sharing entanglement entropy <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>S</mml:mi></mml:math>, by a product state, and we show how to construct modes achieving this minimal energy cost. Thus, we obtain a protocol independent lower bound on the extraction of pure state entanglement from quadratic systems. Due to their generality, our results apply to a large range of physical systems, as we discuss with examples.