Reviewerly/exHarmony huggingface dataset:
Two Types of there-sentences and Feature Specification

This paper aimed to account for unexpected accusative case on a pronominal associate in there-sentences (there was him).It is unexpected that under the long-standing assumption in generative grammar, agreement coincides with case assignment.Since there-associates appear to agree with T in number (e.g.There was/*were a dog), they are expected to be valued as nominative case.Furthermore, such a pronominal associate with accusative case is not available in there-V type sentences (*There arrived him).In this paper, I propose a multiple-there hypothesis to account for different behaviors of there-V and there-BE.In the proposal, I argue that there-s in there-V and there-BE are base-generated in different positions due to their different "grammatical roles" (semantically null expletive vs. subject argument).Based on the distinction, I further argue that there-s have different feature specifications and show that the proposed system captures different behaviors between there-sentences with respect to sub-extraction and control.
A Pilot Study of Chaos Criteria with Hilbert Transform and Mutual Information

Abstract Chaos feature criteria tree has many fruits with geometric maps and calculated values. To easily understand how to study chaos identification, we propose one new learning combination solution based on Hilbert transform (HT) and mutual information (MI). The most important coding probe ( x -axis) used the HT of locked uniformly distributed random number (LUSRN), so far the y -axis in contrast standard map points to LUDRN. The measure between contrast standard map and unknown map (normalized test data, HT of LUSRN) used MI values. The test cases cover five chaos equations and eighteen quasi-period functions. The results show in statistics that our new maps and corresponding MI values can classify three kinds of signals with periodic, chaotic and random states. And this work may promote rapid growth of an apple embedded in novel chaos criteria.
Worldly Eyes on Video: Learnt vs. Reactive Deployment of Attention to Dynamic Stimuli

Computational visual attention is a hot topic in computer vision. However, most efforts are devoted to model saliency, whilst the actual eye guidance problem, which brings into play the sequence of gaze shifts characterising overt attention, is overlooked. Further, in those cases where the generation of gaze behaviour is considered, stimuli of interest are by and large static (still images) rather than dynamic ones (videos). Under such circumstances, the work described in this note has a twofold aim: (i) addressing the problem of estimating and generating visual scan paths, that is the sequences of gaze shifts over videos; (ii) investigating the effectiveness in scan path generation offered by features dynamically learned on the base of human observers attention dynamics as opposed to bottom-up derived features. To such end a probabilistic model is proposed. By using a publicly available dataset, our approach is compared against a model of scan path simulation that does not rely on a learning step.
MathReality: A Bridge from Concrete to Abstract via an AR app for Mathematics Concept of Exponents

Mathematics is a challenging subject for middle school students due to the introduction of concepts that are abstract and unrelatable in a real-world context. Emerging Augmented Reality (AR) technologies afford to visualize those abstract concepts integrated with the physical environment. Hence, we developed an AR app, MathReality, to teach the mathematics concept of exponents in more concrete ways and help them move from concrete to more abstract understanding of the concept. Preliminary results suggest the app was useful to the users and helped them learn the concept easily. The paper concludes with the next steps for the project, in terms of its design, implementation, and evaluation.
A logic of hypothetical conjunction

Abstract A binary connective that can be read as a matching conjunction for conditional connectives found in many conditional logics is considered. The most natural way to read this connective is often as a conjunction and yet, hypothetically, considered to hold of a state of affairs that could be obtained under the hypothesis. The connective can be given an intensional semantics extending a standard semantics of conditional logic that uses propositionally indexed families of binary relations on possible worlds. This semantics is determined by an adjoint relationship between the operations supporting the semantics of the conditional and the new conjunction. The semantics of the hypothetical conjunction connective subsumes the semantics, supported by a ternary relation semantics, of the fusion connective that arises in connection with substructural and relevant logics, and therefore subsumes a number of other forms of conjunction. A number of applications of the hypothetical conjunction connective are discussed, including generalized forms of resource reasoning used in computer science applications.
An Adaptive and Transferable Dialog Management System for Social Aware Task Execution

Efficient and acceptable AI agents need to interact and dialog with their users taking into account not just task efficiency but also social preferences in the interaction. In this work we introduce a model for generating dialog in different scenarios. At the base of the system is a dialog management based on POMDP (Partially Observable Markov Decision Process) models. This task specific component can by itself be used to execute the task. We then introduce another level, and define the interface between the levels, to be able to complement the dialog generated to take into account the social preferences of interaction of each particular user. We show how a particular parameterization of the state allows to learn a personalised policy. We further show that with our formalism a social policy learned for a particular user can then be used in other similar tasks without requiring further learning. We present several simulations showing how we can plan multiple tasks, learn social policies and that those policies can be transferred.
Does every formal peer review really need to take place? An industrial case study

Existing studies on formal peer reviews (FPR) are mostly based on design and code review practices observed from the open source software (OSS) community, and there is a lack of studies and evidence from proprietary, large scale software development in systems engineering settings. This study fills the gap by reporting an industrial case study on FPR practices in a safety-critical embedded system development organization. First, we follow the Goal-Question-Metric (GQM) methodology to establish a set of metrics for understanding FPR practices and the resulting effectiveness. Second, we collect the metric data from 100 programs employing FPR practices on a variety of document types. Third, we analyze the data and derive answers to a set of questions defined in the GQM. The study contributes to more comprehensive understanding of FPR practices and effectiveness evaluation, and the findings reported here may serve as a basis for process improvements initiatives in similar settings.
The Simple Approach to Multi-label Image Classification Using Transfer Learning

The article deals with the problem of image classification on a relatively small dataset. The training deep convolutional neural net from scratch requires a large amount of data. In many cases, the solution to this problem is to use the pretrained network on another big dataset (e.g. ImageNet) and fine-tune it on available data. In the article, we apply this approach to classify advertising banners images. Initially, we reset the weights of the last layer and change its size to match a number of classes in our dataset. Then we train all network, but the learning rate for the last layer is several times more than for other layers. We use Adam optimization algorithm with some modifications. Firstly, applying weight decay instead of L2 regularization (for Adam they are not same) improves the result. Secondly, the division learning rate on the maximum of gradients squares sum instead of just gradients squares sum makes the training process more stable. Experiments have shown that this approach is appropriate for classifying relatively small datasets. Used metrics and test time augmentation are discussed. Particularly we find that confusion matrix is very useful because it gives an understanding of how to modify the train set to increase model quality.
Evaluating an Early Motion Planning Feedforward Sensorimotor Mechanism for 2D Sketching

Movement-based interactive systems, and gesture recognition in general, can contribute to the human learning of motor skills. This paper presents the design and initial evaluation of a sensori-motor learning mechanism that provides on-line feedforward to the user when s/he repeats a gesture. This mechanism is based on real time statistical comparison between the input and reference gesture as well as on early motion planning principles that generate the feedforward. It has been implemented for improving the motor control in 2D sketching. 14 participants were appointed to evaluate whether this mechanism has the ability to impart motor skills.
A Fraudulent Transactions Simulation Method Based on Genetic Algorithm

Abstract When using machine learning or other methods to construct the fraud detection models, the banking industry faces such problems: the number of fraud transactions data is too small, which affect the training of anti-fraud model and the detection effect of fraud transaction. This paper proposed a data simulation algorithm based on genetic algorithm (GA-DS). By studying the feature of real fraudulent transactions, we designed the crossover mutation and other genetic operators, explored the suitable fitness function that can evaluate the quality of simulated data, and generated simulated data satisfying the characteristics of the original transaction. The experiment result shows that mixing the simulated data and the original data into the training can improve the detection ability of anti-fraud model.
Home experiments - A way to increase the interest of young people in physics, technology and natural sciences

This paper is a tribute to my colleague prof. RNDr. Ivan Baník, PhD, who died on July 2, 2017. It was shortly after we decided to work on this article. It took me quite long to continue and finish at least the small part of our planned work. His invention in preparing simple experiments was well known, and this was how we became not only colleagues but friends as well. Our common effort was to make students like physics. One way to increase the interest in physics is to strengthen the experimental component of physics teaching and, in particular, to promote simple experiments that student can also perform independently at home. The simple experiment helps to eliminate mechanical forms of studying, gives more tangible physical content and develops physical thinking. Provides a stimulus for own creative search of physical contexts. Physics is basically experimental, but the experiment is also a natural part of the learning process.
Improving performance of a Computational Neural Network Library

In order to convert a physical problem into a computational one, there should be libraries to support the execution of the program. Insilico is a library used to simulate network of neurons. The simulator numerically integrates a system of coupled non-linear ordinary differential equation (ODE). This study is to provide a stepwise optimization mechanism for the sequential code of this library into a highly parallelized one which can exploit the available processor capabilities and which in turn will effectively reduce the time and increase the efficiency of the application. The one under study is an API to simulate neurons of the olfactory system whose code is highly data dependent with a number of integral calculations. We propose a way of optimizing the application by parallelizing it through OpenMP and efficiently using the massive thread and data parallelism available on the same.
Non-rigid ICP and 3D models for face recognition

3D fitting algorithms are very important for searching particular facial parts and key points, which can be used for recognition of facial expressions with various face deformations. Common non-rigid ICP variants are usually based on the affine point-to-point approach. In this paper, in order to apply a non-rigid ICP to facial surfaces we build a part-based model of the 3D facial surface and combine it with a new non-rigid ICP algorithm. Computer simulation results are provided to illustrate the performance of the proposed algorithm.
Analysis of Keystream Produced by Generalized Shrinking Multiplexing Generator Controlled by Ternary m-sequence

The stream cipher research field is very significant as contemporary communication channels need fast and secure encryption. To ensure unconditional security in stream ciphers the used keystream must be nonlinear and with good pseudo noise properties. By this motivation, in this paper a keystream produced by Generalized Shrinking Multiplexing Generator controlled by Ternary m-sequences (GSMG-3m) is analyzed. Statistical, spectral and approximate entropy tests are done to ensure the randomness properties of keystream sequences produced by the GSMG-3m. Some cryptanalysis of GSMG-3m are done and it is proven that GSMG-3m is more secure than Shrinking Generator (SG) to attacks through its control ternary m-sequence and linear complexity.
Complexity Aspects of Unstructured Sparse Graph Representation

In this paper, we address the problem of the trade-off between the compact memory representation of graphs and their amount of randomness. We design a representation (abbreviated as DBP representation) which does not use information on the structure of graphs, hence it is generally usable. Based on our theoretical lower bound on graph space representation, we define a compression ratio for a given graph with respect to the DBP representation. Based on experimental results, we derive the empirical relationship between the amount of randomness and the compression ratio.
Virtual Reality by Older Adults, Time and Connectedness

In this paper, anthropologist Tanisha Jowsey reviews a virtual reality interactive film from The GeriActivists project, produced by illuminart®. Director of illuminart®, David Ryan, responds.I ofte...
Secure Data Protection Using Slicing as a Confusion Technique

Data Mining deals with automatic extraction of previously unknown patterns from large amounts of data sets. These data sets typically contain sensitive individual information or critical business information, which consequently get exposed to the other parties during Data Mining activities. Secure data protection has been one of the greater concerns in data mining. Several anonymization techniques, such as generalization and bucketization, have been designed for privacy protective microdata publishing. The generalization loses considerable amount of information, especially for high dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. Solution to this problem is provided by we introduce a novel data anonymization technique called slicing to improve the current state of the art.
Physics experiment as a dominant educational element

Physics experiment is one of the most important motivational and educational elements in the teaching of physics. In all of its forms it fulfils irreplaceable roles in school education: it makes learning more attractive to pupils, creates a space for their activity, engages pupils deeper in the process and ultimately contributes to the development of their physics thinking. Last but not least, physics experiments also promote development of social links between pupils and development of the necessary competences (communicative, social, cooperative, etc.). The presented case study answers questions whether the physics experiment brings the expected effects, whether it is beneficial in lessons and whether the use of physics experiments increases the understanding of the problem. To obtain answers to these research questions the methods of both qualitative and quantitative methodology were used. Specifically, questionnaire surveys, video surveys and analyses using the FIAS categorization system. The base data was processed using the usual statistical procedures.
Faulty Diagnosis of Network Video Surveillance System of X Vocational Education Centre, XI’AN

Abstract Aiming at the problems of the projects of government procurement of computer network video surveillance systems in terms of design, construction, management have been existing, this paper would found them through a survey with help of instruments, such as Network Connectivity Tester, etc. diagnosing the faults of the network Video Surveillance System of X vocational education centre in Xi’an. These problems include the faulty of network topology design, not standardized network engineering construction, material use issues and problems of not clear video surveillance images, and the author makes recommendations related to repair of the video surveillance system for accumulating experiences for improving management of government procurement project and raising the quality of network integration construction.
Modal Filter for Spacecraft Busbar Protection Against Ultrashort Pulses

Protection of onboard equipment of a spacecraft against electromagnetic interference is an actual problem. One of the most dangerous effects is an interfering ultrashort pulse. The use of known devices for protection against conducted interference is hampered by a number of conflicting requirements, for example, protection of as many circuits as possible, small mass of a protective device, and the ability to function effectively for 15 years in space conditions. The protection of equipment against the ultrashort pulse, based on modal filtering is proposed. The paper presents the results of the development of a modal filter prototype with a broad-side coupling. When choosing a structure, such parameters as the maximum bus current, insulation breakdown voltage, wave impedance of the line and the duration of the interfering ultrashort pulse were taken into account. To substantiate the choice of structure, quasi-static and electrodynamic simulations were performed. The simulation results showed that the modal filter effectively attenuate the 0.45-ns ultrashort pulse by 7 times. A modal filter prototype without resistors has been implemented, which showed a greater level of attenuation of the ultrashort pulse than the resistive construction.
Crystallomorphology of Cassiterite and Its Practical Importance

The methods of applied mineralogy are used to assess the scale of the mineralization of tin occurrences and deposits. One of such methods is the crystallomorphological method for estimating the level of tin ore bodies developed by N.Z. Evzikova. According to the crystallomorphological features of cassiterite it allows one to estimate the possible depth of distribution of mineralization and its magnitude. Using the example of three studied tin-ore districts of the Far East the possibilities of using crystallomorphology of cassiterite to predict and evaluate the prospect of mineralization of both the ore region as a whole and individual ore occurrence at the exploration stage are shown.
Knowledge-Based Optimization Algorithm

The extraction of knowledge corresponds to the process of finding hidden information such as spatial and context relationships from a given bunch of data. Recently, the extraction of knowledge represents one of the most challenging tasks in several research fields, since the correct representation, preprocessing and generalization of data are crucial steps for finding relevant information. Poor representation of data may result into information redundancies, bad preprocessing operations yield to the presence of outliers, while poor generalization step tend to produce overfitted models. One of the most interesting unsupervised learning models are the well-known Self-Organized Maps (SOMs), which represents the data using only certain number of dimensions while they keep the topology relationships among the data. These operations produce a learning model robust to data invariance. In this chapter, it is presented the hybridization of a SOM with the main concepts of evolutionary computation to produce a metaheuristic algorithm called (EA-SOM). The proposed method involves the dimensionality reduction scheme and topology preservation of the input data to conduct the search strategy into promissory search zones within the search space of a given optimization problem. The proposed metaheuristic method guarantees faster convergence, reliable results, and scalability than several state-of-art evolutionary algorithms. The numerical results suggest that EA-SOM overcomes its competitors in terms of accuracy, robustness and convergence speed.
A study on reducing power consumption of the centrifugal fan for the negative pressure adsorption wall climbing robot

Abstract In order to solve the problems of ship wall climbing robot’s great power consumption and high noises, a new approach to reducing power consumption and noises of the centrifugal fan was investigated. Firstly, the relationship between the air volume of a centrifugal fan and ship’s dip angle of the outer wall is obtained by analyzing force condition of the robot and the theory of negative pressure adsorption. Furthermore, by means of researching the process of fan’s speed-regulation through changing DC 0-3.3 of fan’s driver. We get the relationship between speed-regulation voltage after D/A convert of fan’s driver and ship’s dip angle of the outer wall. We proposed a control system that can automatically detect ship’s dip angle of the outer wall and adjust the output power of the centrifugal fan. Finally, the result of reducing power consumption and noises was tested effectively.
WITHDRAWN: SCHEMA: A Discrete Cross-Modal Hashing by Preserving Multiple Similarities

Abstract Recently, cross-modal hashing has attracted much attention. To learn hash codes, many supervised cross-modal hashing methods construct a large-size semantic pairwise similarity matrix and reconstruct it by hash codes, which is time-consuming and neglects the similarity between high-level and low-level features. In addition, the binary constraints of hash codes make the optimization problem NP-hard. Most methods relax the binary constraints, leading to large quantization error. To address these issues, in this paper, we present a novel cross-modal hashing method, i.e., diScrete Cross-modal Hashing by prEserving Multiple similArities, SCHEMA for short. It embeds multiple types of similarity into the learning of binary codes, i.e., the high-level similarity and the low-level similarity. In the light of this, the binary codes may preserve more similarity information of the samples in the original space. In addition, to solve the optimization problem, it equivalently transforms the binary constraints into an intersection of two continuous spaces. Thereafter, the problem is solved with a proposed algorithm without relaxation, avoiding the large quantization error problem. Moreover, the computational complexity of its training is linear to the size of a training set, making it scalable to large-scale datasets. Extensive experimental results on four benchmark datasets demonstrate SCHEMA consistently outperforms some state-of-the-art hashing methods by large gaps.