Reviewerly/exHarmony huggingface dataset:
Development of Teaching Materials for Learning "Measurements and Controls via Programs" in Technology Education

In the junior high school learning guideline (technology education) since FY2012, there is "D-(3) Automatic measurements and controls via computer programs" in "Technology of information processing" and it is compulsory. In this study, the following developments were made and evaluated on the contents of “measurement and control” instructed in junior high school technology education. (1) Learning contents and goals of measurement and control, and (2) Development of teaching materials such as illumination light and sensor car. In developing these teaching materials and teaching materials, I considered "the relationship between our lives and technology" and "the practical and hands-on learning activities".
Ensemble Methods for Improving Classification of Data Produced by Latent Dirichlet Allocation

Topic models are very popular methods of text analysis. The most popular algorithm for topic modelling is LDA (Latent Dirichlet Allocation). Recently, many new methods were proposed, that enable the usage of this model in large scale processing. One of the problem is, that a data scientist has to choose the number of topics manually. This step, requires some previous analysis. A few methods were proposed to automatize this step, but none of them works very well if LDA is used as a preprocessing for further classification. In this paper, we propose an ensemble approach which allows us to use more than one model at prediction phase, at the same time, reducing the need of finding a single best number of topics. We have also analyzed a few methods of estimating topic number.
Modified VS<sup>3</sup>-NET for Reading Comprehension and Question Answering with No-Answers

Span-based machine reading comprehension of question answering (MRQA) is basically composed of an encoding module for the question and passage respectively, and a question-passage matching module to locate the answer in the passage using an attention mechanism. Recently, the newly published SQuAD 2.0 is a span-based MRQA for dealing with unanswerable questions. In this paper, we solve three issues of current practices in SQuAD 2.0: (1) modules that encode slowly, (2) type-unaware questions encoding module with unanswerable questions, and (3) a non-compositional matching module. We propose a modified VS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> -NET model. This modified model is based on co-attention with contextual embedding using simple recurrent unit (SRU) instead of other recurrent neural network (RNN) to speed up training and test while retaining the recurrency of the model. Moreover, it uses variational inference to infer the question type with the unanswerable questions, uses a hierarchical matching and scoring module, and uses a gated module for features and a context vector. Experiments show that the modified VS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> -NET provides outstanding improvements over the base model performance and produces performances competitive with the state-of-the-art models on SQuAD v2.0.
One Shot Learning with Margin

One shot learning is a task of learning from a few examples, which poses a great challenge for current machine learning algorithms. One of the most effective approaches for one shot learning is metric learning. But metric-based approaches suffer from data shortage problem in one shot scenario. To alleviate this problem, we propose one shot learning with margin. The margin is beneficial to learn a more discriminative metric space. We integrate the margin into two representative one shot learning models, prototypical networks and matching networks, to enhance their generalization ability. Experimental results on benchmark datasets show that margin effectively boosts the performance of one shot learning models.
Selective Information Extraction Strategies for Cancer Pathology Reports with Convolutional Neural Networks

To trust model predictions, it is important to ensure new data scored by the model comes from the same population used for model training. If the model is used to score new data different than the model’s training data, then predictions and model performance metrics cannot be trusted. Identifying and excluding these anomalous data points is an important task when using models in the real world. Traditional machine learning algorithms and classifiers don’t have the capability to abstain in this case. Here we propose a data-novelty detection algorithm for the Convolutional Neural Network classifier, yielding a rejection score for each new data point scored. It is a post-modeling procedure which examines the distribution of convolution filters to determine if the prediction should be trusted. We apply this algorithm to an information extraction model for a natural language text corpus. We evaluated the algorithm performance using a primary cancer site classification model applied to cancer pathology reports. Results demonstrate that the algorithm is an effective way to exclude cancer pathology reports from model scoring when they do not contain the expected information necessary to accurately classify the primary cancer type.
Code Generation from Declarative Models of Robotics Solvers

This short paper describes a development methodology addressing the limitations of traditional robot kinematics and dynamics software libraries. Specifically, the implementation choices which hinder the integration of the libraries within user code based on different assumptions; for example, choices about the mathematical formalism or the digital data types. Code generation based on declarative and semantically unambiguous specification is proposed as a more flexible development approach, which allows to configure the generated API and the concrete implementation choices. The paper also introduces a prototype tool that we developed to investigate our research hypothesis, and discusses some of the current challenges.
Homogeneous Ensemble Methods for the Prediction of Number of Faults

Software testing isHomogeneous ensemble methods intended to find bugs/faultsFaults that can occur in the software components currently under development. Software fault prediction (SFP) helps in achieving this goal by predicting the probability of fault occurrence in the software modules before the testing phase.
The content and types of research work of higher educational institutions students of Russia in the second half of the twentieth century and in modern conditions

The article analyzes the content and types of scientific research work of students of higher educational institutions of Russia in the second half of the 20th century and in modern conditions; the structure of research activity of students is determined, which ensures the effectiveness of the educational process and promotes the development of professional qualities.
Efficient Cryptanalytic Hardware

The cryptosystems rely on the hardness of their underlying complex computational problems, that should be unconquerable with ordinary computing machines. Successful cryptanalytic attacks can be launched with the help of custom-built high-performance computing platforms, cryptanalytic weaknesses can aid it further. COPACOBANA is one such custom hardware optimized for executing cryptanalytical Algorithms [1]. Other reported works include General Purpose Processors (GPPs), ASICs, GPUs and ASIPs used as a platform to attack a cryptographic algorithm.
Quantitative Analysis Method of Color Gamut Difference Between Digital Image Originals

Digital imaging is an important means of making image originals in the field of media reproduction and dissemination today. The gamut of digital image original not only restricts the accuracy of color reproduction in the traditional hard copy, but also the quality of the hard copy would be affected directly. In order to scientifically and quantitatively analyze the digital image original gamut, this paper uses unsupervised clustering analysis method. Comparing various imaging environment factors affecting the digital image original gamut through the designed experiment, and some good applications for digital imaging and digital image original evaluation are obtained.
Mutual reinforced part‐aligned network for person re‐identification

Part misalignment of the human body caused by complex variations in viewpoint and pose makes person re-identification be a fundamental challenging task. In this Letter, the authors propose a new Part-Aligned Network (PAN) stacked by Part-Aligned Module (PAM) to learn the effective representation for person re-identification. Specifically, PAN is constructed by stacking several PAMs which is simultaneously comprised of an appearance branch and a part branch. The appearance branch performs spatial modelling while the part branch captures human part information simultaneously. Appearance features and part features are then combined to explore the implicit complementary advantages and these two branches are learned in a mutually reinforcing way. Extensive experiments are conducted on the three most popular datasets: Market-1501, DukeMTMC-reID, and CUHK03. The experimental results demonstrate that the proposed PAN achieves superior performance to the existing state-of-the-art methods.
Restoring Heisenberg scaling in noisy quantum metrology by monitoring the environment

We study quantum frequency estimation for qubits subjected to independent Markovian noise, via time-continuous monitoring of the environment, showing, among other things, that perfectly efficient detection allows for restoring Heisenberg limit.
How to Authenticate MQTT Sessions Without Channel and Broker Security

This chapter describes a new but state-of-the-art approach to provide authenticity in MQTT sessions using the means of zero-knowledge proofs. This approach completely voids session hijacking for the MQTT protocol and provides authenticity. The presented approach does not require the broker to keep any secrets for session handling. The presented approach allows completely anonymous but authentic sessions; hence, the broker does not need any priory knowledge of the client party. As it is especially targeted for applications within the world of internet of things (IoT), the presented approach is designed to require only the minimum in extra power in terms of energy and space. The approach does not introduce any new concept, but simply combines a state of the art cryptographic Zero-Knowledge Proof of identity with the existing MQTT 5.0 specification. Thus, no protocol extension is required in order to provide the targeted security properties. The described approach is completely agnostic to the application layer at the client side and is only required during MQTT session establishment.
The Design Development Research of Computer Music System Based on College Vocal Music

Computer music has become one of the important means in modern music teaching because of its simple and easy to operate characteristics. The combination of computer music technology and multimedia has brought great convenience to our music teaching. It not only updates the traditional teaching mode, expansion the teaching content, but also makes the traditional music theory to be realistic and lively. It greatly enlarge the students’ musical vision, activates the students’ musical viewing, promote the students’ enthusiasm for learning, and greatly improves the efficiency of music learning.
Small-Scale Image Inpainting on Mobile Platform

In the digitization of literature, especially in the electronic storage of library books, the remake of documents and the repair of defects are increasingly important. In this paper, based on the needs of library remake, a JavaScript algorithm is used to design and develop a repair algorithm for retrieving image information using a digital camera on a mobile platform. It achieves the repair of damaged books, improves readers’ reading experience and reaches the application goal of cross-platform, low equipment cost, and low algorithm complexity.
Latent Gaussian-Multinomial Generative Model for Annotated Data

Traditional generative models annotate images by multiple instances independently segmented, but these models have been becoming prohibitively expensive and time-consuming along with the growth of Internet data. Focusing on the annotated data, we propose a latent Gaussian-Multinomial generative model (LGMG), which generates the image-annotations using a multimodal probabilistic models. Specifically, we use a continuous latent variable with prior of Normal distribution as the latent representation summarizing the high-level semantics of images, and a discrete latent variable with prior of Multinomial distribution as the topics indicator for annotation. We compute the variational posteriors from a mapping structure among latent representation, topics indicator and image-annotation. The stochastic gradient variational Bayes estimator on variational objective is realized by combining the reparameterization trick and Monte Carlo estimator. Finally, we demonstrate the performance of LGMG on LabelMe in terms of held-out likelihood, automatic image annotation with the state-of-the-art models.
Syntactic n-grams: The Concept

As we have already mentioned, the main idea of the formal features applicable in computational linguistics is related to the vector space model and the use of n-grams as features in this space, which also includes unigrams, i.e., words. The words are considered in the contexts. Usually, it is neighbor words. But some words that have syntactic relations are not neighbors, thus, here appears the idea to use syntactic information to obtain real (syntactically related) neigbors as the context of a word. So, we suggest obtaining n-grams by following paths in sytanctic trees.
Analysis of the Application and the Association Rule Mining in the Library and Data Management of College of Economics

University libraries have always been regarded as the information service centers of universities. Teachers’ teaching and scientific research and students’ extracurricular reference services are inseparable from the library’s collection of literature resources. With the increasingly prominent personalized needs of readers, the service orientation of the libraries of the College of Economics in colleges and universities is gradually transforming from the traditional provision of the explicit information to the personalized services that actively mine the implicit data information in order to meet the needs of the readers. The emergence of the data mining technology has brought opportunities to the personalized services of libraries. Through the analysis of the massive data, we can find the potential relationship between data.
Influence of Image Noise on Digital Photo Definition

This study aimed to investigate the cognition of object texture formation and subjective preference in image evaluation. The relationship between the addition of noise and the definition of natural objects in digital photos was studied. Two experiments were designed on the basis of the psychophysical experimental and objective evaluation methods. Psychological scales were used to quantify the relationship between noise level and image definition. The results of objective evaluation indicated that the definition of the digital image gradually deteriorated as noise levels increased. The results of subjective evaluation revealed that not all observers perceived an improvement in image definition with the addition of noise.
Innovation and Design

The present research features the relationship between the two processes in design. First, more and more areas of culture reveal their design character. Second, design activity is getting more and more innovative. This is due to the increasing attention to design in science and practice, in particular, in cultural studies. This relationship is interpreted as a separate subject of research, which has the following constituents: a) cultural conditions and factors, b) the process of implementation of technical and technological innovations in the socio-cultural fabric of society and man. Thus, there is a problem of their mutual determination, which can be investigated by such methods of analysis as idealization and modeling. Creativity is represented as one of the manifestations of culture as a whole and is expressed in the conceptual vision of design and continuity of socio-cultural design. The authors focus on the matter of socio-cultural support and effective implementation and assimilation technical and technological innovations by culture, society, and man by creating certain conditions and factors. The concept of the design configurator is used here as a model of the process represented in the problem. The paper enumerates the functional features of management objectivity, pragmatism, efficiency, creativity, heuristics, etc., which make it possible to focus on methodological approaches not only in problem solving but also to argue the importance of the design approach in the understanding of the relationship of these socio-cultural sides of human existence. The design configurator is not only a synthesis of different types of subject knowledge, but rather a starting point in the methodological work on the formation of epistemological portrait of the created artifact, i.e. technological innovations using such methodological tools as socio-cultural and design approach.
Steganography and Cryptography for User Data in Calendars

Privacy is the abilityVörös, Péter of an individualHudoba, Péter or group to seclude themselves, or their privateKiss, Attila information, and thereby express themselves selectively. Nowadays the increasing usage and popularity of web services indicate the likelihood of privacy disclosures. Data breaches are the number one privacy threat when using cloud services. To avoid the possibility for data theft users should not trust anyone including the service providers. If they do not rely on the providers’ defense methods, they have to make sure that outgoing traffic remains a secret for everyone. In this paper, we propose our model and our prototype implementation for a client-side data encoder, that aims to solve the privacy disclosure problems. We show different binarization (character string to binary string) and encoding methods (binary string to character string), which with, we can provide secure and hidden data encoding by translating the user data into different sentences that can be considered as valid data. We make sure that only encoded data can be stored in the providers’ storage, therefore we not just ensure that no one has direct access to unencoded data, but we are able to conceal the very existence of the encoding at all.
Video saliency detection based on low-level saliency fusion and saliency-aware geodesic

We present a spatiotemporal saliency detection method for videos. In contrast to previous methods that focus on exploiting the different underlying saliency cues or ignore motion information, the proposed method aims to use both appearance information based on spatial edges and spatial color saliency and motion information based on temporal motion boundaries as indicators of foreground object locations. Spatial color saliency is obtained from the fusion of three color features: color edge connectivity, color rarity, and color compactness. Subsequently, we further smooth the color saliency to eliminate background noises and to further boost the detection accuracy. Then, we propose a strategy-based low-level saliency fusion that guarantees to complementarily employ the smoothed color saliency, spatial edges, and temporal motion boundaries clues toward producing high-accuracy low-level saliency. Subsequently, we generate framewise spatiotemporal saliency maps using a geodesic distance from the low-level saliency. Subsequently, high-quality results are obtained through the geodesic distance to the background area in the subsequent frames. Extensive quantitative and qualitative experiments on three public video datasets demonstrate the superiority of the proposed method over the state-of-the-art algorithms.
Use of Particle Multi-Swarm Optimization for Handling Tracking Problems

As prior work, several multiple particle swarm optimizers with sensors, that is, MPSOS, MPSOIWS, MCPSOS, and HPSOS, were proposed for handling tracking problems.Due to more efficient handling of these problems, in this chapter we innovate the strategy of information sharing (IS) to these existing methods and propose four new search methods that are multiple particle swarm optimizers with sensors and information sharing (MPSOSIS), multiple particle swarm optimizers with inertia weight with sensors and information sharing (MPSOIWSIS), multiple canonical particle swarm optimizers with sensors and information sharing (MCPSOSIS), and hybrid particle swarm optimizers with sensors and information sharing (HPSOSIS).Based on the added strategy of information sharing, the search ability and performance of these methods are improved, and it is possible to track a moving target promptly.Therefore, the search framework of particle multiswarm optimization (PMSO) is established.For investigating search ability and characteristics of the proposed methods, several computer experiments are carried out to handle the tracking problems of constant speed I type, variable speed II type, and variable speed III type, which are a set of benchmark tracking problems.Owing to analyze experimental results, we reveal the outstanding search performance and tracking ability of the proposed search methods.
Enhancing the Resolution of Satellite Images Using the Best Matching Image Fragment

Due to very high costs and a long revisit time, it is challenging to obtain good quality satellite images of the area of interest. As a result, super resolution reconstruction (SRR) methods which allow for creating a high-resolution (HR) image based on single or multiple low-resolution (LR) observations are being extensively developed. In this paper, we propose a few improvements to well-known single-image SRR technique based on a dictionary of pairs of matched LR and HR image fragments. The modifications concern both increasing the number of pairs of images fragments and the reconstruction algorithm itself in order to achieve visually pleasing results. This allows us to increase the quality of newly produced HR satellite images what is supported by conducted experiments.
On Some Approach to Evaluation in Personalized Document Retrieval Systems

Due to the information overload in the Internet, it is a hard task to obtain relevant information. New techniques and sophisticated methods are developed to improve efficiency of the searching process. In our research, we focus on a Personalized Document Retrieval System which allows to adjust relevance of searched documents. Based on user data, usage data and social connections between users, it determines up-to-date user profile and recommends better documents. In the work we analyze a methodology for experimental evaluations in simulated environment.
Chapter 2. New words in contemporary Chinese language use

This study analyzes the linguistic features and word formation processes of the new words in The List of New Words Used in Media 2015. The results show that the average word length of the 446 new words used in media 2015 is 3.34, with both 3- and 4-morpheme words hovering around 40% of the total and 2-morpheme words under 17%. The majority of the 2-morpheme new words parallel the Chinese syntactic patterns, such as [modifier + modified], [subject + predicate], [verb + object], and [verb + verb]. The major processes involved in the 4-morpheme word formation are blending, abbreviation, coinage, and numerical formulae. In the blending and abbreviation processes, large chunks of information are clipped off to maintain the [2+2] 4-morpheme word length pattern. In addition, like many other newly-created usages, the case of 互联网+ shows that, in language change, new words can be created through grammaticalization and various types of derivational morphology, involving the creation of new affixes.