Reviewerly/exHarmony huggingface dataset:
Aplicaciones de la tecnología blockchain en la documentación científica: situación actual y perspectivas

Blockchain technology is being introduced in all areas and procedures of verification, identification and recording of the virtual world. Some of its applications are simple translations of methods that are already being developed in other technical supports, but most of them provide new utilities that can change the technological context of scientific documentation. In this article we present an updated panorama of the initiatives based on blockchain for the management of scientific and technological documentation, highlighting those aspects that make this new technology a differentiated and authentically novel tool. A critical analysis with theoretical and practical considerations on its potential use completes the content of this work.
Mitigating Cold Starts in Serverless Platforms: A Pool-Based Approach

Rapid adoption of the serverless (or Function-as-a-Service, FaaS) paradigm, pioneered by Amazon with AWS Lambda and followed by numerous commercial offerings and open source projects, introduces new challenges in designing the cloud infrastructure, balancing between performance and cost. While instant per-request elasticity that FaaS platforms typically offer application developers makes it possible to achieve high performance of bursty workloads without over-provisioning, such elasticity often involves extra latency associated with on-demand provisioning of individual runtime containers that serve the functions. This phenomenon is often called cold starts, as opposed to the situation when a function is served by a pre-provisioned "warm" container, ready to serve requests with close to zero overhead. Providers are constantly working on techniques aimed at reducing cold starts. A common approach to reduce cold starts is to maintain a pool of warm containers, in anticipation of future requests. In this report, we address the cold start problem in serverless architectures, specifically under the Knative Serving FaaS platform. We describe our implementation leveraging a pool of function instances, and evaluate the latency compared to the original implementation, resulting in a 85% reduction of P99 response time for a single instance pool.
Modeling Recurrence for Transformer

Recently, the Transformer model that is based solely on attention mechanisms, has advanced the state-of-the-art on various machine translation tasks. However, recent studies reveal that the lack of recurrence hinders its further improvement of translation capacity. In response to this problem, we propose to directly model recurrence for Transformer with an additional recurrence encoder. In addition to the standard recurrent neural network, we introduce a novel attentive recurrent network to leverage the strengths of both attention and recurrent networks. Experimental results on the widely-used WMT14 English-German and WMT17 Chinese-English translation tasks demonstrate the effectiveness of the proposed approach. Our studies also reveal that the proposed model benefits from a short-cut that bridges the source and target sequences with a single recurrent layer, which outperforms its deep counterpart.
Correlated-Source Extractors and Cryptography with Correlated-Random Tapes

In this paper, we consider the setting where a party uses correlated random tapes across multiple executions of a cryptographic algorithm. We ask if the security properties could still be preserved in such a setting. As examples, we introduce the notion of correlated-tape zero knowledge, and, correlated-tape multi-party computation, where, the zero-knowledge property, and, the ideal/real model security must still be preserved even if a party uses correlated random tapes in multiple executions. Our constructions are based on a new type of randomness extractor which we call correlated-source extractors. Correlated-source extractors can be seen as a dual of non-malleable extractors, and, allow an adversary to choose several tampering functions which are applied to the randomness source. Correlated-source extractors guarantee that even given the output of the extractor on the tampered sources, the output on the original source is still uniformly random. Given (seeded) correlated-source extractors, and, resettably-secure computation protocols, we show how to directly get a positive result for both correlated-tape zero-knowledge and correlated-tape multi-party computation in the CRS model. This is tight considering the known impossibility results on cryptography with imperfect randomness. Our main technical contribution is an explicit construction of a correlated-source extractor where the length of the seed is independent of the number of tamperings. Additionally, we also provide a (non-explicit) existential result for correlated source extractors with almost optimal parameters.
Causality in Linear Logic

Abstract Commuting conversions of Linear Logic induce a notion of dependency between rules inside a proof derivation: a rule depends on a previous rule when they cannot be permuted using the conversions. We propose a new interpretation of proofs of Linear Logic as causal invariants which captures exactly this dependency. We represent causal invariants using game semantics based on general event structures, carving out, inside the model of [6], a submodel of causal invariants. This submodel supports an interpretation of unit-free Multiplicative Additive Linear Logic with MIX (MALL $$^-$$ ) which is (1) fully complete : every element of the model is the denotation of a proof and (2) injective : equality in the model characterises exactly commuting conversions of MALL $$^-$$ . This improves over the standard fully complete game semantics model of MALL $$^-$$ .
Automatic Stitching of Medical Images Using Feature Based Approach

Image stitching is a process of creating a panoramic image by combining multiple images that have overlapping regions of the same scene.It is a challenging topic in image processing, multimedia, and medical applications.The proposed system can be applied in medical applications for scoliosis operations and other long limb operations.The problem of the traditional x-ray machine is the narrow view.So, it can't produce the large view of body x-ray image in a single frame.Therefore, this problem is solved by combining two or more x-ray images into a panorama one.This paper proposes a system which automatically stitches the x-ray images and produced a panorama x-ray image.The proposed stitching method is based on feature based approach, ORB (Oriented FAST and Rotated BRIEF).In feature detection stage, Oriented FAST approach is used.In feature description stage, Rotated BRIEF approach is applied.The two important criterias for determining the stitching performance are stitched image quality and processing time.Therefore, the aim of my proposed system is to produce a panorama x-ray image with high resolution and low processing time based on feature extraction approach.We compared our proposed method with three different features detectors.SIFT, SURF, and Harris corner detectors were tested and measured the rate of correct features detection and computation time.Finally, we measured the quality of result images that produced by stitching system of different feature based methods.According to the experimental results, ORB approach can produce high quality panorama image with least processing time.
Vision-based autonomous landing of a quadrotor using a gimbaled camera

This paper proposes a novel vision-based autonomous landing scheme for micro aerial vehicles with perturbations by using a gimbaled camera. There are no sensors available on the moving target in the task. The relative position between the drone and moving target is obtained by the camera mounted on a two-degree-of-freedom pan-tile platform. Firstly, the detection algorithm runs in real time and outputs the pixel tracking errors. Then, an adaptive vision-based controller for the pan-tilt platform guarantees that the line of sight of the camera always tracks the target. Next, the relative position tracking error is approximated according to the gimbal's rotation angles. Disturbance observer-based control is applied for flight control to attenuate the effect of disturbance and recover the hign tracking performance, where relative velocity and lumped disturbances are estimated by extend disturbance observers. The proposed flight controller guarantees that the tracking errors are ultimately bounded with tunable ultimate bounds. The convergence property is demonstrated through Lyapunov theory. The simulations and experiments illustrate the effectiveness and the superiority performance of the proposed control system.
Regularizing Activation Distribution for Training Binarized Deep Networks

Binarized Neural Networks (BNNs) can significantly reduce the inference latency and energy consumption in resource-constrained devices due to their pure-logical computation and fewer memory accesses. However, training BNNs is difficult since the activation flow encounters degeneration, saturation, and gradient mismatch problems. Prior work alleviates these issues by increasing activation bits and adding floating-point scaling factors, thereby sacrificing BNN's energy efficiency. In this paper, we propose to use distribution loss to explicitly regularize the activation flow, and develop a framework to systematically formulate the loss. Our experiments show that the distribution loss can consistently improve the accuracy of BNNs without losing their energy benefits. Moreover, equipped with the proposed regularization, BNN training is shown to be robust to the selection of hyper-parameters including optimizer and learning rate.
The Development of Digital Book of European History to Shape the Students’ Democratic Values

This study aims to develop digital book based on sigil on the material of European history in growing the values of Pancasila democracy. This book is aimed to give an alternative of learning material by using ICT to reach the aims of learning. The book should follow the standardized test to ensure the digital book could be used in teaching and learning process at History Department, State University of Malang. This study tended to used research and development approach. We used eight Sadiman’s model of (1) identification of needs; (2) composing aims; (3) material development; (4) development of evaluation tool; (5) writing a draft; (6) validation; (7) revision; (8) production. Based on data analysis, this study found that the book could be used without any major revision. The percentage of questionnaire result from media expert was 90.48 % and from material expert was at 96.75 %. To short, the digital book has filled the requirement of book standardization. Therefore, the digital book based on sigil could be used as learning source for the module of European History.
Collaborative Three-Dimensional Completion of Color and Depth in a Specified Area With Superpixels

A persistent problem in the three-dimensional (3-D) reconstruction technique is to eliminate blank areas in the 3-D map, which commonly emerges after removing undesired objects, such as dynamic targets or occluded areas. This task is challenging for it is difficult to acquire the coherence between color and depth information, which are both lost for each pixel in the target region. Moreover, creating novel artifacts also needs to be avoided during the completion process. To address these problems, in this paper, we propose a collaborative method to complete the lost area in the color image and its corresponding depth map. In the proposed method, an examplar-based image inpainting technique combined with planarity knowledge is adopted to iteratively repair the texture and structure information. The color completion process provides candidates for plane-fitting on segmented superpixels. The depth filling step extracts image areas requiring reinpainting, which is defined by their gradient differences. The final 3-D map can then be reconstructed from both completed color and depth images. Experiments on real-world scenes demonstrate the success of our method in the completion of 3-D maps.
Diagnostical tools to evaluate student’s creative thinking development

We show the data of diagnostic research, main aim is to analyze the development level of structural components of students creative thinking. Creative thinking is interpreted as a profes-sionally-oriented thinking characterized by the ability to generate a lot of original ideas in unregu-lated conditions of activity, developed verbal creativity and insight, a high level of reflection, in-quisitiveness, imagination, self-control of emotional states. We consider it as the most important factor influencing personal and professional self-change of students – future psychologists. We re-veal the indicators of creative thinking development and the diagnostic tools for their evaluation are substantiated, identify and analyze the main problem areas in the development of creative thinking of students (low level of verbal activity, lack of curiosity, lack of understanding of the need for further work on the development of imagination). Using diagnosis we reveal the relation-ship between the individual psychological characteristics of the personality of students-psychologists and their predominant types of thinking: subject, symbolic, symbolic and imagina-tive. We obtain the analysis results using a five-factor personal questionnaire by R. McCrae and P. Costa which allows to make a personal portrait of the subjects by many factors: extra – version – introversion, attachment – isolation, self-control – impulsiveness, emotional stability-instability, expressiveness-practicality. We reveal the difficulties in the development of creative thinking, which meet students with a focus on the prevalence of one of the indicators in a particular pair. We present the factor analysis results of the obtained data carried out by principal component analysis with the construction of the correlation matrix and subsequent orthogonal rotation. Correlation analysis showed that the students' scale of creativity correlates with such indicators as imagination, creative thinking, activity – passivity, domination – submission, manifestation – avoidance of guilt, extroversion – introversion, credulity – suspicion, curiosity – conservatism, curiosity – realism, activity component. The results of the study can be used in the educational programs development aimed at the students’ creativity development and personal and professional competencies formation of future psychologists in high school.
Comparative analysis of software-constructors of tests and their use in the educational process

The article provides a comparative analysis of software quality .test designers based on the ISO-9126 quality model according to such quality criteria as functionality, reliability, usability, efficiency, ease of maintenance and portability. The advantages and disadvantages of software quality are revealed and the prospects for further research are determined.
The use and trend of emotional language in the banks’ annual reports: the state of the global financial crisis

This study is of an exploratory nature as it seeks to explore the extent to which the language of emotions in the banks’ annual reports is affected by the global financial crisis (GFC). The language of emotions was analyzed using eight categories (trust, anticipation, sadness, anger, fear, disgust, surprise and joy) in annual reports of 12 listed banks from six countries in the Middle East area (namely, Jordan, Kingdom of Bahrain, United Arab Emirates, Sultanate of Oman, Kuwait, Kingdom of Saudi Arabia) from 2002 to 2017. The final data set consists of 192 bank-year observations. The study time was divided into three periods (pre, during and post GFC). In addition, the study enriches accounting literature by being the first study to test Pollyanna hypothesis using emotion analysis. The results of the study show that the percentage of emotional words in banks’ annual reports (2002–2017) represents almost 22% on average. The trust, anticipation and fear categories were the most affected than other emotional categories during GFC. While the trust category decreased, both the fear and anticipation categories increased. Other findings of the study show that regardless of GFC, emotional words of trust and anticipation categories in banks’ annual reports have dominated the emotional words of the disgust and surprise categories. Therefore, Pollyanna hypothesis is supported. In contrast to the emotional words of the joy category in banks’ annual reports which has not dominated the sadness category. In this case, Pollyanna hypothesis is rejected.
Security Measure for Image Steganography Based on High Dimensional KL Divergence

Steganographic security is the research focus of steganography. Current steganography research emphasizes on the design of steganography algorithms, but the theoretical research about steganographic security measure is relatively lagging. This paper proposes a feasible image steganographic security measure based on high dimensional KL divergence. It is proved that steganographic security measure of higher dimensional KL divergence is more accurate. The correlation between neighborhood pixels is analyzed from the principle in imaging process and content characteristics, and it is concluded that 9-dimensional probability statistics are effective enough to be used as steganographic security measure. Then in order to reduce the computational complexity of high dimensional probability statistics and improve the feasibility of the security measure method, a security measure dimension reduction scheme is proposed by applying gradient to describe image textures. Experiments show that the proposed steganographic security measure method is feasible and effective and more accurate than measure method based on 4-dimensional probability statistics.
Short note on the behavior of recurrent neural network for noisy dynamical system

The behavior of recurrent neural network for the data-driven simulation of noisy dynamical systems is studied by training a set of Long Short-Term Memory Networks (LSTM) on the Mackey-Glass time series with a wide range of noise level. It is found that, as the training noise becomes larger, LSTM learns to depend more on its autonomous dynamics than the noisy input data. As a result, LSTM trained on noisy data becomes less susceptible to the perturbation in the data, but has a longer relaxation timescale. On the other hand, when trained on noiseless data, LSTM becomes extremely sensitive to a small perturbation, but is able to adjusts to the changes in the input data.
Systematization of Intellectual Property Objects for Forensic Purposes

Correlation of the concepts of the object of forensic intellectual property investigation and the object of intellectual property is contemplated. The attributes of these objects are analyzed, in particular the intangible nature of intellectual property objects and the tangible character of the objects of forensic intellectual property investigation. A distinction is made between the concepts of ‘intellectual property’, ‘industrial property’ and ‘intangible assets’. The proposed systematization of intellectual property objects for forensic purposes is based on current practices in court ordered forensic intellectual property investigations. The case is made for setting up a new species of forensic investigation in the system of the Russian Ministry of Justice, as well as four new forensic science specialties: (1) investigation of items covered by copyright and related rights, (2) investigation of objects of patent rights and unconventional objects, (3) investigation of brand identities, (4) valuation of intellectual property objects.
The effect of tablet use on students success in English as a Foreign Language (EFL) grammar classroom

Integration of tablets in teaching English as a Second or Foreign language has been popular in recent years.However, there is limited evidence to prove the effect of tablet use on the acquisition of specific language skills.This paper aims to investigate the impact of tablet use on students' mastery of grammar skills.In order to answer the research questions, an experimental pretest-posttest with a control group design was employed at a private university in Turkey.The pre-test scores of both groups were compared with their post-test scores, and the post-test scores of the groups were also analyzed to look into a possible significant difference.The results indicate that there is no significant difference between the grammar achievement scores of the students in both groups.The findings were also cross-checked by using the views of the instructor and students of the experimental group on tablet use in the classroom.The instructor emphasized the influence of tablet use in learner autonomy, digital distraction, and network connection.Students mainly indicated that tablets can be supplementary, but they should not replace basic course materials such as textbooks and workbooks.The study can help raise awareness of curriculum designers and decision-makers generally about the effect of tablet use in the language classroom.
Learning Subword Embedding to Improve Uyghur Named-Entity Recognition

Uyghur is a morphologically rich and typical agglutinating language, and morphological segmentation affects the performance of Uyghur named-entity recognition (NER). Common Uyghur NER systems use the word sequence as input and rely heavily on feature engineering. However, semantic information cannot be fully learned and will easily suffer from data sparsity arising from morphological processes when only the word sequence is considered. To solve this problem, we provide a neural network architecture employing subword embedding with character embedding based on a bidirectional long short-term memory network with a conditional random field layer. Our experiments show that subword embedding can effectively enhance the performance of the Uyghur NER, and the proposed method outperforms the model-based word sequence method.
Art - <i>Subject - Object</i> in Artistic and Plastic Creativity of Pupils and Students in Artistic Education

Abstract The modernization of higher education in the field of plastic arts methodology, the improvement and development of the educational methodology, as well as the professional rise of the teachers are the key issues in raising the level of creativity of the future plastic art teachers in the context of the new requirements of artistic training. The volume of creative knowledge and skills varies from one person to another; it is our permanent intention to know and understand creativity, to demonstrate the laws of artistic and plastic creativity, to create a logical theory through which personality can be developed creatively, thus contributing to the complex process of the student’s artistic activity. Art contributes to the development of the artistic creativity of pupils / students, constituting a source of knowledge of national and universal culture through the many methodological, plastic and technological varieties specific to art, which offer vast possibilities to create a creative product of artistic, aesthetic and cultural value. The development of creative writing skills initially involves a visual experience, followed by the stages specific to the creative process in the field of fine arts and is finalized by a material form of the creative product, obtained through specific means in the field of fine arts.