Reviewerly/exHarmony huggingface dataset:
Maximizing Computer Based Test (CBT) Teslet Model for Education Quality Mapping

Abstract Mapping the quality of education is one step that can be done to anticipate and overcome problems in the world of education. Basically, the mapping of the quality of education is aimed at equitable distribution of education. To achieve these objectives, it is necessary to maximize the development of three aspects of quality consisting of inputs, processes and outputs. The process as an important part of the responsibility of teachers has an extraordinary role in gathering various information or data needed to determine the quality or the quality of education at a particular institution or school. Therefore, one solution that can be taken which is to develop a test model based on computer, commonly known as Computer Based Testing (CBT). CBT is a form of software that can be developed using a computer. The development of the CBT in question must also be accompanied by a quality problem model so that the policies taken can be effective. The test model testlet is an alternative that can be maximized considering that this test is a collaboration between the form of problem and multiple-choice questions with the level of difficulty of questions that are tied. By using this testlet, the teacher can not only obtain information about student competencies, but can also obtain information about individual characteristics. In addition, the use of this test model will make it easier for governments and educators to conduct quality mapping because it is more effective and efficient
Neurocomputing approach to predict 2D ferromagnetic lattices by spin dynamics

An artificial neural networks modeling, based on spin dynamics, has been proposed to predict the 2D ferromagnetic crystals. The purpose is to establish an artificial neural network model (ANNM), which is able to predict the ordered magnetic surfaces. In crystal surfaces, Wood’s notation is used to describe different configurations. From the magnons dispersion curves and group velocity data of 2D spins crystallographic waveguide, we deduce the data used as an up input–output set to feed the ANNM. The input data are being given by the magnons modes over high symmetry points, or, by the group velocity of the magnon modes. However, the basis vectors of matrix notation and the rotation angle that aligns the unit cell of the reconstructed surface are giving the output data. The proposed neurocomputing model was capable of predicting the 2D crystals for the possible arrangements that the surface might take. In addition, the magnon excitations data were a useful approach for building a neural model which was able to predict and classify the magnetic surfaces of the crystals.
Invisibility and Fidelity Vector Map Watermarking Based on Linear Cellular Automata Transform

Invisibility and fidelity influence 2D vector maps, especially the way vector data is used after applying various watermarking techniques to obtain a veiling of the digital vector map’s information via distortion control. This study proposes a linear cellular automata technique to safeguard vector maps for copyright protection purposes. Performance evaluation of the proposed system indicates higher invisibility and fidelity compared to previous frequency techniques. Additionally, the proposed technique indicates that, in digital watermarking, it is possible to use several frequency domains.
Context-Dependent Token-Wise Variational Autoencoder for Topic Modeling

This paper proposes a new variational autoencoder (VAE) for topic models. The variational inference (VI) for Bayesian models approximates the true posterior distribution by maximizing a lower bound of the log marginal likelihood of observations. We can implement VI as VAE by using a neural network called encoder to obtain parameters of approximate posterior. Our contribution is three-fold. First, we marginalize out per-document topic probabilities by following the proposal by Mimno et al. This marginalizing out has not been considered in the existing VAE proposals for topic modeling to the best of our knowledge. Second, after marginalizing out topic probabilities, we need to approximate the posterior probabilities of token-wise topic assignments. However, this posterior is categorical and thus cannot be approximated by continuous distributions like Gaussian. Therefore, we adopt the Gumbel-softmax trick. Third, while we can sample token-wise topic assignments with the Gumbel-softmax trick, we should consider document-wide contextual information for a better approximation. Therefore, we feed to our encoder network a concatenation of token-wise information and document-wide information, where the former is implemented as word embedding and the latter as the document-wide mean of word embeddings. The experimental results showed that our VAE improved the existing VAE proposals for a half of the data sets in terms of perplexity or of normalized pairwise mutual information (NPMI).
Research on User Experience Design Consistency of Internet Products Based on User Experience

Abstract In recent years, with the continuous development of network technology, our daily life has been inseparable from the Internet. The increasing popularity of various terminal devices has brought about a variety of system and screen sizes. How to maintain the consistency of user experience among a large number of device terminals has attracted more and more attention. As a bridge between human beings and Internet products, whether the interface design is satisfactory to users fundamentally affects the use of products, which is also a hot research topic at home and abroad. Based on the research of multi-screen interactive design, combined with the consistency theory and the construction method of consistent experience, this paper proposes to construct multi-screen interactive consistent experience by seeking commonness from the micro level and emphasizing the use of scenes from the macro level.
From Humans and Back: a Survey on Using Machine Learning to both Socially Perceive Humans and Explain to Them Robot Behaviours

As intelligent robots enter our daily routine, it is important to be equipped with proper adaptable social perception and explainable behaviours. To do so, machine learning (ML) is often employed. This paper intends to find a trend in the way ML methods are used and applied to model human social perception and produce explainable robot behaviours. The literature has shown a substantial advancement in ML methods with application to social perception and explainable behaviours. There are papers which report models for robots to imitate humans and also for humans to imitate robots. Others use classical methods and propose new and/or improved ones which led to better human-robot interaction performances. This paper reports a review on social perception and explainable behaviours based on ML methods. First, we present literature background on these three research areas and finish with a discussion on limitations and future research venues.
Exploiting Linearity in White-Box AES with Differential Computation Analysis

Not only have all current scientific white-box AES schemes been mathematically broken, they also face a family of attacks derived from traditional Side Channel Attacks, e.g., Differential Computation Analysis (DCA) introduced by Bos et al. Such attacks are very universal and easy-to-mount – they require neither knowledge of the implementation, nor use of reverse engineering. In this paper, we particularly focus on DCA against white-box AES by Chow et al. which shows lower than $$100\%$$ success rate as opposed to other schemes studied by Bos et al. We provide an explanation of this phenomenon while unraveling another weakness in the design of white-box AES by Chow et al. Based on our theoretical results, we propose an extension of the original DCA attack which has a higher chance of key recovery and might be adapted for other schemes.
A high-capacity invertible steganography method for stereo image

Invertible steganography is a kind of information hiding, in which a secret message is embedded in a carrier in such a way that the human visual system is less sensitive. Moreover, the original media is completely retrieved after the secret message is extracted. This chapter proposes a novel invertible steganography scheme with high embedding capacity using two-dimensional histogram shifting in the transform domain. We investigated that the DCT-quantized coefficients of each pair of similar blocks in the left and right views of stereo image commonly meet to (−1,0,1) values, and the secret bits are first partitioned into 3-bit groups and encoded into decimal form. The embedding direction histogram (EDH) is built and used for embedding. By using EDH to shift the middle frequency DCT-quantized coefficients of the two-dimensional histogram the proposed scheme archives the trade-off between imperceptibility and embedding capacity. The experimental results indicated the method had better performance in comparison to previous schemes.
Why LASSO, Ridge Regression, and EN: Explanation Based on Soft Computing

In many practical situations, observations and measurement results are consistent with many different models–i.e., the corresponding problem is ill-posed. In such situations, a reasonable idea is to take into account that the values of the corresponding parameters should not be too large; this idea is known as regularization. Several different regularization techniques have been proposed; empirically the most successful are LASSO method, when we bound the sum of absolute values of the parameters, ridge regression method, when we bound the sum of the squares, and a EN method in which these two approaches are combined. In this paper, we explain the empirical success of these methods by showing that these methods can be naturally derived from soft computing ideas.
Chinese Aspect-Level Sentiment Analysis CNN-LSTM Based on Mixed Vector

Aspect-level sentiment analysis can provide more detailed information than general sentiment analysis, because it aims to predict the sentiment polarities of the given aspects or entities in text. The state-of-the-art neural models use RNN with attention seems a good method for the characteristics of this task in English, but because of the complexity of Chinese texts and the difference between Chinese and English, English learning outcomes cannot be directly applied to Chinese. After re-examining the drawbacks of attention mechanism and obstacles that Chinese aspect-level sentiment analysis, we build upon line of research and propose three approaches overcome these issues. First, we propose a mixed vector to enhance the information embedded in Chinese word embedding. Second, we introduce an attention weight calculate method for target representation that better captures the semantic meaning of the opinion target. Third, we use CNN to extract local information in the text. The experimental results show that our model consistently outperforms the state-of-the-art methods on Chinese reviews.
To the Question of Almost Maximum-Likelihood Decoding

Evseev’s lemma claims that if we use the decoding by a standard array and use coset leaders having the weight not greater than Gilbert-Varshamov distance then the probability of error decoding will become not greater than the probability of error decoding of maximum-likelihood multiplied by two. However, the estimation of tightness of this bound is an open problem. In this work we perform the estimation of the accuracy of Evseev’s lemma on the example of several linear random codes by simulating them in a binary-symmetric channel.
Mobile shooting assistant based on intelligent vehicle

Abstract Most shooting casinos still stay in a fixed-point shooting state. Some mobile shooting is just laying rails in the shooting range and running by cable. This type of target is too limited and not flexible enough. This kind of moving target is based on smart car. A robotic arm is installed on the smart car to grip the target. Different end points are set in the field. Each time the smart car starts, an end point is randomly generated and the path is identified by the camera. Drive to the end to form a moving target, and design a shock absorption device to prevent the vehicle body from vibrating violently when the target is hit.
Chapter 9. Reviewing manuscripts for academic journals

Reviewing manuscripts for academic journals not only contributes to the advancement of knowledge and the development of fellow researchers, but also holds numerous advantages for the reviewers themselves. After a brief explanation of what reviewing is and how it works, this chapter describes some of the benefits of reviewing; provides guidance regarding how to write fair, reasonable, meaningful, systematic, specific, improvement-oriented reviews; raises factors to consider in decision-making; offers advice for balancing reviewing against other professional priorities and obligations; and seeks to answer a variety of practical “how-to” and “what-if” questions that might arise in the process. Approached with a constructive and strategic mindset, reviewing offers valuable opportunities for graduate students and new faculty to grow intellectually and professionally, influence and promote progress in the field, enhance their reputations, and establish themselves as engaged members of a research community in ways that will stand them in good stead throughout their careers.
Towards Dynamic Process Composition in the DSO Cognitive Architecture

In recent works, the DSO Cognitive Architecture’s design is enhanced by incorporating the concept of the Global Workspace Theory (GWT). The theory proposes that consciousness is realised through the competition of massive, specialised, parallelised processes and thus parallelised, unsynchronised cognitive processes become sequential through such bottleneck. Due to the concurrent nature of DSO Cognitive Architecture, coordination of the different parallel processes through this competition mechanism can be difficult and if not handled properly, will create inconsistent results. In this work, we propose a preliminary framework to coordinate the different processes by process composition which borrows concepts from automated planning. Processes, its argument signature and its output are abstracted into higher level type abstractions which can be used to compose with other processes based on matching the output types to argument types. This is known as process composition and it represents a sketch of how different process can coordinate with one another. We combined this with the current design of the DSO Cognitive Architecture and illustrate an example in crowd anomaly detection.
Automatic Generation of Abstracts in Scientific Articles Based on Natural Language Processing for Early Education Professionals and Speech Therapists

Currently, millions of articles are generated around the world on different topics or areas and published in different scientific databases. Generally, access to this repository uses search tools based on keywords that generate a list of results where sequential access is applied to generate an understanding that involves a great effort of search, classification, and selection of scientific articles within an area or specific topic. That is why, in this work, an intelligent computer system is presented that allows us to automatically obtain summaries of articles based on search keywords, which is ordered hierarchically according to the content belonging to the search keywords. Finally, this work is aimed at speech therapists and initial educators so they can have new information about tools, new trends, applications, methodologies, etc. and in turn be supportive or helpful in classrooms or speech therapies.
Research on Information Hiding Based on Intelligent Creation of Tang Poem

Text is the most important and frequent way for people to exchange information and daily communication in today’s society; thus, text information hiding has great research value and practical significance. This paper explored a novel method of information hiding based on intelligent creation of Tang poem. Based on the construction of meaning intention vocabulary, and by using the recurrent neural network language model, the proposed steganography method can effectively generate carrier Tang poem which confidential information embedded in it. In our method, each line can hide 9-bit sensitive information. The hidden capacity of the five-character Tang poem is 11.25%. Experiments showed that this algorithm had relatively high carrying capacity and concealment.
Performance Improvement of Clustering Affinity Propagation Method using Principal Component Analysis

Abstract Affinity Propagation Method it is necessary to modify the algorithm by using Principal Component Analysis (PCA). PCA method is used to reduce the attributes or characteristics that are less influential on the data so that the most influential attributes are obtained to then be carried out the clustering process with Affinity Propagation. The comparison results of the PCA + AP grouping model have better performance than the conventional AP grouping model. This is justified because the number of iterations and clusters produced by the PCA + AP clustering model does not change and converges when there are 8 optimal cluster clusters. While the performance of conventional clustering models produces an optimal number of clusters from 14 clusters with a significant number of iterations. So it can be concluded that the PCA + AP grouping model is suitable for the Air Quality dataset because it produces an optimal number of clusters and iterations of 8 clusters. The comparison results of the PCA + AP grouping model have better performance than the conventional AP grouping model. This is justified because the number of iterations and clusters produced by the PCA + AP clustering model does not change and converges when the optimal number of clusters is 5 clusters. While the performance of conventional clustering models produces a suboptimal number of 10 clusters with a significant number of iterations. So it can be concluded that the PCA + AP grouping model is suitable for the Water Quality Status dataset because it produces an optimal number of clusters and 5 cluster repetitions.
LOCI: A Mobile Q&amp;A System with Multimodal Motivation Scheme for Local Intent Questions in Dynamic Social Networks

Recent research has shown that people are frequently searching for information with local intent. The existing systems fail to answer this class of questions as they are not mainly designed to handle it. In this paper, we propose LOCI, a mobile question & answer system with a multimodal motivation scheme for local intent questions where users provide timely and accurate answers in a dynamic social network. LOCI mines users' shared and contextual information to identify the most relevant users for each question. The proposed multimodal motivation scheme exploits users' social ties and monetary rewards to raise their basic motivation levels. LOCI assigns questions to the best answerers via our three proposed algorithms: (i) LOCI-RT, which immediately assigns newly arrived questions/users to available users/questions, (ii) LOCI-LO, which uses short-time batches for locally optimal questions-users assignment, and (iii) LOCI-AD, which assigns short- and long-deadline questions immediately and via batching, respectively. We conduct: (i) a survey to support our assumptions regarding the proposed multimodal motivation scheme, and (ii) trace-driven experiments to evaluate the performance of the three proposed LOCI's algorithms.
DiaTTroD: A Logical Agent Diagnostic Test for Tropical Diseases

Medical diagnosis is one of the critical areas in medicine. Diagnosing tropical diseases can be confusing if their signs and symptoms are similar. This paper presents a formal logic for constructing a diagnostic test capable of guiding patient examination. A logical agent based on morphological data is used to aid diagnostic procedures and decision-making. To ensure that the appropriate diagnostic procedure is undertaken, the signs and symptoms of the patient are examined first before deciding which exact laboratory examination is needed by the patient. The logical agent perceives the signs, symptoms, medical history, and environment of the patient. Its actuation includes request for laboratory examination. A test kit result can be used, if available, to further confirm the diagnosis. The decision is not based on statistical inference but on logical analysis of the perceived data. Since not all signs and symptoms are present at a certain point in time, using this logical agent will aid the user in diagnosing the patient. A developed test case is presented and result is shown. Test results show 100% accuracy for diseases present in the knowledge base. Also, this paper shows the importance of using morphology in correctly diagnosing a disease. Digital image processing, if completely embedded in this logical agent, will guide the agent in correctly identifying the disease.
Predictive Modelling of Type Valency in Word Formation Grammar

This paper explores different regression models for predicting the type valency of Persian suffixes within a usage-based approach. Usage-based models treat the type frequency of a suffix as a key predictor for its type valency revealing that an increase in the type frequency leads to a greater combining power between a construction’s paradigmatic elements. However, this effect is limited to a certain degree by the potential productivity of a suffix, as inferred from the statistically distinguishable negative correlation between the type valency and the potential productivity, as well as from the statistical significance of the variable of the number of hapaxes and the potential productivity in the regression models of conditional inference trees. Moreover, polyvalency as a distinct feature of Persian derivation implies a number of other characteristics, namely greater morphological diversity of patterns, parsability, semantic transparency and larger conversion power of morphemes. This is contrasted with English whose morphemes are predominantly type-monovalent.
Ecosystem of Ecosystems in InterCity Biking

Digitalization, megatrends, the new opportunities offered by technologies have caused societies to break through towards the fourth industrial transformation. It will change the whole society and its structures alongside the business. All societies, not just corporations, but also cities and regions, have to prepare for the change caused by digitalization. That challenges also traditional organizational structures. This requires a new type of business operation and organization. Cities are responding to the rapid progress of society and are moving from traditional organizational structures to ecosystem development. They are building their own network to involve in developing new operations. While cities largely receive their own funding as taxes, development and change management must be considered in the same way as in businesses. A key measure of efficiency is the improved service and achieved cost efficiency. The article examines the city biking ecosystem development on the Finnish Growth Corridor.
Automatic Detection of Pedestrians in Traffic Scene Images

This article is devoted to automatic detection of pedestrians using a deep neural network. To achieve this goal, the deep neural network model DeepLabv3+ was adapted to the task of segmenting the road traffic scenes. The possibility of reduction of training sets by means of application of training transfer followed by fine tuning was of the primary interest.