Reviewerly/exHarmony huggingface dataset:
Discriminative multi-modal deep generative models

Multi-modal learning is of practical importance for real world datasets with heterogeneous features. Most of existing multi-modal algorithms aim to learn shared representations that can maximally extract the correlations among multi-modal data in an unsupervised manner. Deep variational canonical correlation analysis-private (DVCCA-private) is an efficient multi-modal generative learning model in which each modality of data is generated by “common” variables underlying in both modalities and “private” variables within specific modality. After training, the inferred common representations are used to train classifier and competitive performance can be achieved. For many multi-modal datasets, every modality may contain specific discriminative information compared with other modalities and the common information among modalities may be irrelevant to discriminate. In this paper, we propose a discriminative multi-modal deep generative model (DMDGM), in which each modality of data is generated by “label” variables underlying in both modalities and “private” variables. The proposed model can separate discriminative information from discrimination-insensitive information. We derive variational lower bounds of the data likelihood and train the model by maximizing this lower bound. The proposed model combines representation learning and classifier training in a unified framework and the inferred “label” representations are directly used for label prediction without additional classifier. The empirical results on Noisy-MNIST, XRMB and NUS datasets show that the proposed DMDGM can significantly improve the prediction performance compared with previous multi-modal deep generative models.
EVSO: Environment-aware Video Streaming optimization of Power Consumption

Streaming services gradually support high-quality videos for the better user experience. However, streaming high-quality video on mobile devices consumes a considerable amount of energy. This paper presents the design and prototype of EVSO, which achieves power saving by applying adaptive frame rates to parts of videos with a little degradation of the user experience. EVSO utilizes a novel perceptual similarity measurement method based on human visual perception specialized for a video encoder. We also extend the media presentation description, in which the video content is selected based only on the network bandwidth, to allow for additional consideration of the user’s battery status. EVSO’s streaming server preprocesses the video into several processed videos according to the similarity intensity of each part of the video and then provides the client with the processed video suitable for the network bandwidth and the battery status of the client’s mobile device. The EVSO system was implemented on the commonly used H.264/AVC encoder. We conduct various experiments and a user study with nine videos. Our experimental results show that EVSO effectively reduces the energy consumption when mobile devices uses streaming services by 22% on average and up to 27% while maintaining the quality of the user experience.
Fairness in dataflow scheduling in the cloud

Expensive dataflow queries which may involve large-scale computations operating on significant volumes of data are typically executed on distributed platforms to improve application performance. Among these, cloud computing has emerged as an attractive option for users to execute dataflows allowing them to select proper configurations (e.g., number of machines) to achieve desired trade-offs between execution time and monetary cost. Discovering dataflow schedules that exhibit the best trade-offs within a plethora of potential solutions can be challenging, especially in a heterogeneous environment where resource characteristics like performance and price can be varied. To increase resource utilization, users may also submit multiple dataflows for execution concurrently. Traditionally, building fair schedules (schedules where the slowdown of all dataflows due to resource sharing is similar) while achieving good performance is a major concern. However, considering fairness in the cloud computing setting where monetary cost is part of the optimization objectives significantly increases the difficulty of the scheduling problem. This paper proposes an algorithm for the scheduling of multiple dataflows on heterogeneous clouds that identifies Pareto-optimal solutions (schedules) in the three-dimensional space formed from the different trade-offs between overall execution time, monetary cost and fairness. The results show that in most cases the proposed approach can provide solutions with fairer schedules without significantly impacting the quality of the execution time to monetary cost skyline compared to the state of the art where the fairness of a solution is not taken into account.
Financing of young knowledge-based companies after the financial crisis: the case of Kazakhstan

Following the financial crisis of 2008, more attention is paid to access to finance for young knowledge-based firms. A study conducted before the crisis showed that it was more difficult for knowledge-based firms to gain access to finance. However, none of the studies considered the differential effect of the crisis on young knowledge-based firms. This document examines this gap using the data set of 1155 firms in Kazakhstan for various sectors of the economy. The analysis was carried out in the context of the years before the crisis of 2002-2005 and after the crisis, when the economy significantly stabilized for 2014-2017. We believe that young knowledge-based firms are more likely to be rejected for financing than other firms, and this has significantly worsened in the post-crisis period. However, regressions that control a variety of characteristic characteristics show that the deterioration in the general conditions for financing. The results show that there are two questions in the financial system. The first is a structural problem that restricts access to finance for young knowledge-based firms. Secondly, the cyclical problem was caused by the financial crisis and significantly affected the young knowledge-based firms of various sectors of the economy.
Toondoo Application Based on Contextual Approach: Development of Comic Learning Media

This research aims to develop a comic learning media in the form of ToonDoo Applications in the subject of the circular motion. This research uses research and development methods by using a model from Borg & Gall that is adapted by Sugiono. The data of this study were obtained from the results of questionnaire responses of students and educators, validation questionnaires for material experts, media experts, and linguists. The type of data produced is qualitative data which is analyzed with criteria for assessment categories to determine product quality. The feasibility of comic physics books using the Toondoo application is based on a contextual approach according to experts in the "very feasible" category with an average score score of 94.19% material experts, 89.46% media experts, 93% and 83.33%. To use comic book physics using the Toondoo application based on a contextual approach according to students and educators in the "very interesting" category with an average score of 85.61% in small group trials, 86.01% field trials and educator experience testing 88, 59%.
Optical Encryption and Decryption

The application of artificial light to carry information, that is, to transmit signals over longer distances, dates from the use of fire to send messages. In the 1880s, Bell demonstrated his system for transmitting speech using a modulated beam of light. This invention paved the way for more complex light communications over distances of several hundred meters. However, such techniques only became widespread with the invention and implementation of the optical laser. The availability of coherent (monochromatic) light sources has led to the development of modern light-based, dense data transmission. Since then, many methods for transmitting information optically have been proposed and investigated. For example, initially, laser beams were shone through free space (i.e., the atmosphere). This approach has some drawbacks, for instance, system unreliability due to natural atmospheric effects (rain, snow, fog, and turbulence). These difficulties indicate why the propagation of light waves within shielded environments, such as through gas-filled pipes and tubes containing sequential arrangements of optical lenses, have been investigated. Of such approaches, optical fibers provide the best method of channeling light over long distances. For instance, a 550,000-mile optic fiber cable is hidden under the ocean transmitting vast quantities of information at high speed across the globe. However, in the vacuum of outer space, wave guidance is necessary, and in 2013, as part of the first demonstration of free-space laser communication, NASA scientists beamed an image of the Mona Lisa to the Lunar Reconnaissance Orbiter, a satellite orbiting the moon, from Earth.
Stark and Kerr Effects on the Dynamics of Moving N-Level Atomic System

We have investigated numerically the dynamics of quantum Fisher information (QFI) and quantum entanglement (QE) for N-level atomic system interacting with a coherent field in the presence of Kerr (linear and non-linear medium) and Stark effects.It is observed that the Stark and Kerr effects play a prominent role during the time evolution of the quantum system.The evolving quantum Fisher information (QFI) is noted as time grows under the non-linear Kerr medium contrary to the QE for higher dimensional systems.The effect of non-linear Kerr medium is greater on the QE as we increase the value of Kerr parameter.However, QFI and QE maintain their periodic nature under atomic motion.On the other hand, linear Kerr medium has no prominent effects on the dynamics of N-level atomic system.Furthermore, it has been observed that QFI and QE decay soon under the influence of Stark effect.In short, the N-level atomic system is found prone to the change of the Kerr medium and Stark effect for higher dimensional systems.
Health assistant: answering your questions anytime from biomedical literature

Abstract Motivation With the abundant medical resources, especially literature available online, it is possible for people to understand their own health status and relevant problems autonomously. However, how to obtain the most appropriate answer from the increasingly large-scale database, remains a great challenge. Here, we present a biomedical question answering framework and implement a system, Health Assistant, to enable the search process. Methods In Health Assistant, a search engine is firstly designed to rank biomedical documents based on contents. Then various query processing and search techniques are utilized to find the relevant documents. Afterwards, the titles and abstracts of top-N documents are extracted to generate candidate snippets. Finally, our own designed query processing and retrieval approaches for short text are applied to locate the relevant snippets to answer the questions. Results Our system is evaluated on the BioASQ benchmark datasets, and experimental results demonstrate the effectiveness and robustness of our system, compared to BioASQ participant systems and some state-of-the-art methods on both document retrieval and snippet retrieval tasks. Availability and implementation A demo of our system is available at https://github.com/jinzanxia/biomedical-QA.
Ground-Based Cloud Recognition Based on Dense_SIFT Features

Clouds play an important role in modulating radiation processes and climate changes in the Earth's atmosphere. Currently, measurement of meteorological elements such as temperature, air pressure, humidity, and wind has been automated. However, the cloud's automatic identification technology is still not perfect. Thus, this paper presents an approach that extracts dense scale-invariant feature transform (Dense_SIFT) as the local features of four typical cloud images. The extracted cloud features are then clustered by K-means algorithm, and the bag-of-words (BoW) model is used to describe each ground-based cloud image. Finally, support vector machine (SVM) is used for classification and recognition. Based on this design, a nephogram recognition intelligent application is implemented. Experiments show that, compared with other classifiers, our approach has better performance and achieved a recognition rate of 88.1%.
Unmanned Aerial Vehicle Perception System Following Visual Cognition Invariance Mechanism

Humans have a fundamental ability that is fulfilling visual perception with complex brain functions, while intelligent sensors onboard UAVs do not have. The difficulties mainly lie in the inexhaustive study of brain science and the homologous mathematical description. In this paper, a visual cognition invariance mechanism has been proposed, which first review studies in brain research, moving from theoretical to practical. These terms are then reconsidered from brain pathways of visual perception. Next, a conceptual model of visual cognition invariance mechanism is proposed and the mathematical deduction is fulfilled. Furthermore, an experimental unmanned aerial vehicle is built to practically implement the proposed algorithm. The simulation results and experimental practices have validated the effectiveness and celerity of our method. Finally, a general discussion and proposals for addressing future issues are given.
‘The fact they knew before I did upset me most’: Essentialism and normativity in lesbian and gay youths’ coming out stories

This article demonstrates, via discourse analysis of a group of young gay and lesbian people’s coming out stories, the salience of essentialist ideologies on their identity construction. The study reveals underlying normative assumptions in the young people’s narratives, including those associated with binary gender and innate sexual desire, which they employ in order to construct a culturally authentic sexual identity. Through close sociolinguistic analysis of interactions, it is shown how identity construction is directly influenced by broader ideologies. The analysis provides evidence of the continued prevalence of heteronormativity and homonormativity as key influences in young queer people’s identity work.
Overview of Digital Image Restoration

Image restoration is an image processing technology with great practical value in the field of computer vision. It is a computer technology that estimates the image information of the damaged area according to the residual image information of the damaged image and carries out automatic repair. This article firstly classify and summarize image restoration algorithms, and describe recent advances in the research respectively from three aspects including image restoration based on partial differential equation, based on the texture of image restoration and based on deep learning, then make the brief analysis of digital image restoration of subjective and objective evaluation method, and briefly summarize application of digital image restoration technique in the future and prospects, provide direction for the research on image after repair.
Unpredictability and robustness of chaotic dynamics for physical random number generation

Random number generation is a fundamental technology behind information security. Recently, physical random number generators (RNGs), which especially harness optical chaos such as in delay-feedback lasers, have been studied intensively. Although these are promising technologies for future information security, there is little theoretical foundation. In this paper, we newly introduce a mathematical formulation of physical RNGs based on a model of chaotic dynamics and give the first rigorous results. In particular, by combining ergodic theory, information theory, and response theory of statistical physics, our theory guarantees, for the model of chaotic dynamics, the coexistence of two crucial properties necessary for physical RNGs: fast random number generation and robustness. Compared with other types of physical RNGs, our theoretical findings highlight an unnoticed advantage of chaotic dynamics utilized for physical RNGs.
The Effect of Tangible Augmented Reality Interfaces on Teaching Computational Thinking: A Preliminary Study

Teaching introductory computer science is gradually shifting from learning a computer programming language towards acquiring more generic computational thinking skills. At the same time, more emphasis is placed nowadays in natural and playful approaches that can potentially increase student motivation and engagement. One promising such approach is the combination of tangible elements with augmented reality technology, where the instructions can be given in the real world by manipulating physical elements, and the output is presented in a digitally enhanced space. Despite its potential, this approach has not yet been evaluated in formal educational settings. In this paper we present the results from a preliminary study in an elementary school that compared a tangible AR game with the same game in an unplugged version, to examine the effect of the interface on student motivation, effectiveness and teaching practice. The results indicate that a tangible AR approach can improve the engagement and collaboration of students in classroom activities and affects the role of the instructor compared to unplugged activities. The paper concludes with a number of open issues that need to be further studied.
LightChain: A DHT-based Blockchain for Resource Constrained Environments

As an append-only distributed database, blockchain is utilized in a vast variety of applications including the cryptocurrency and Internet-of-Things (IoT). The existing blockchain solutions have downsides in communication and storage efficiency, convergence to centralization, and consistency problems. In this paper, we propose LightChain, which is the first blockchain architecture that operates over a Distributed Hash Table (DHT) of participating peers. LightChain is a permissionless blockchain that provides addressable blocks and transactions within the network, which makes them efficiently accessible by all the peers. Each block and transaction is replicated within the DHT of peers and is retrieved in an on-demand manner. Hence, peers in LightChain are not required to retrieve or keep the entire blockchain. LightChain is fair as all of the participating peers have a uniform chance of being involved in the consensus regardless of their influence such as hashing power or stake. LightChain provides a deterministic fork-resolving strategy as well as a blacklisting mechanism, and it is secure against colluding adversarial peers attacking the availability and integrity of the system. We provide mathematical analysis and experimental results on scenarios involving 10K nodes to demonstrate the security and fairness of LightChain. As we experimentally show in this paper, compared to the mainstream blockchains like Bitcoin and Ethereum, LightChain requires around 66 times less per node storage, and is around 380 times faster on bootstrapping a new node to the system, while each LightChain node is rewarded equally likely for participating in the protocol.
Automatic essay scoring with recurrent neural network

As deep learning has developed rapidly in recent years, the automatic essay scoring system, based on deep learning models, has become more reliable than previous feature-based systems. Recent researchers have developed an approach based on recurrent neural networks to learn the relationship between an essay and its assigned score, without any feature engineering. In this paper, we use an ASAP essay dataset, combining feature scoring and a recurrent neural network. The results show that we can compare the result of quadratic weighted Kappa of each experience to get the best model. GloVe significantly improves the results, and feature extraction can affect the result slightly. In future work, we will apply transfer learning, one-shot learning, and adversarial inputs in our model to get better performance.
Comparison of Genetic Programming Methods on Design of Cryptographic Boolean Functions

The ever-increasing need for information security requires a constant refinement of contemporary ciphers. One of these are stream ciphers which secure data by utilizing a pseudo-randomly generated binary sequence. Generating a cryptographically secure sequence is not an easy task and requires a Boolean function possessing multiple cryptographic properties. One of the most successful ways of designing these functions is genetic programming. In this paper, we present a comparative study of three genetic programming methods, tree-based, Cartesian and linear, on the task of generating Boolean functions with an even number of inputs possessing good values of nonlinearity, balancedness, correlation immunity, and algebraic degree. Our results provide a comprehensive overview of how genetic programming methods compare when designing functions of different sizes, and we show that linear genetic programming, which has not been used for design of some of these functions before, is the best at dealing with increasing number of inputs, and creates desired functions with better reliability than the commonly used methods.
Vid2Game: Controllable Characters Extracted from Real-World Videos.

We are given a video of a person performing a certain activity, from which we extract a controllable model. The model generates novel image sequences of that person, according to arbitrary user-defined control signals, typically marking the displacement of the moving body. The generated video can have an arbitrary background, and effectively capture both the dynamics and appearance of the person. The method is based on two networks. The first network maps a current pose, and a single-instance control signal to the next pose. The second network maps the current pose, the new pose, and a given background, to an output frame. Both networks include multiple novelties that enable high-quality performance. This is demonstrated on multiple characters extracted from various videos of dancers and athletes.
Logics for First-Order Team Properties

In this paper, we introduce a logic based on team semantics, called $$\mathbf {FOT}$$, whose expressive power coincides with first-order logic both on the level of sentences and (open) formulas, and we also show that a sublogic of $$\mathbf {FOT}$$, called $$\mathbf {FOT}^\downarrow $$, captures exactly downward closed first-order team properties. We axiomatize completely the logic $$\mathbf {FOT}$$, and also extend the known partial axiomatization of dependence logic to dependence logic enriched with the logical constants in $$\mathbf {FOT}^\downarrow $$.
Hybrid Public Key Encryption

This document describes a scheme for hybrid public-key encryption(HPKE). This scheme provides authenticated public key encryption ofarbitrary-sized plaintexts for a recipient public key. HPKE works forany combination of an asymmetric key encapsulation mechanism (KEM),key derivation function (KDF), and authenticated encryption withadditional data (AEAD) encryption function. We provide instantiationsof the scheme using widely-used and efficient primitives.