Reviewerly/exHarmony huggingface dataset:
An Improved Discretization-Based Feature Selection via Particle Swarm Optimization

Feature selection (FS) aims to remove the redundant or irrelevant features of the data, which plays a very important role in data mining and machine learning tasks. Recent studies focus on integrating the data discretization technique into the FS process to help achieve superior performances in classification. In this paper, we proposed an improved discretization-based FS method via particle swarm optimization to obtain a higher classification accuracy with a smaller size of feature subset. In our approach, we use a novel encoding and decoding way for particle swarm optimization (PSO) which can efficiently select multiple cut-points for discretization. In addition, a new updating strategy and a local search procedure is proposed to strengthen the searching ability and avoid being trapped into local optimal. Experimental results on benchmark datasets demonstrate the efficacy of our proposed methods both in the classification accuracy and the feature subset size.
Two, Three and Four Dimensional BB84: A Comparative Analysis Based on C# Simulation

Quantum Cryptography, the principles of which are based on the laws of quantum mechanics, is a new approach to secure communication. The main advantages of quantum cryptography as opposed to classical cryptography are: unconditional security, the transmission of an encryption key of a length equal to that of the message, the impossibility of creating an identical copy of an unknown quantum state, and the disruption of the quantum system when trying to measure it. BB84 is the first and best known quantum protocol. It was created in 1984 by Charles Bennett and Gilles Brassard and represents a quantum key distribution. The basic idea of the protocol is to encode bits using photon polarization, one bit for each photon. The paper presents an improvement of the protocol by using quantum memory and coding two, three or four bits for each photon, achieving a great efficiency and a higher security level.
A Reliable Distribution Quality Measure in Image Registration

The accuracy of feature-based image registration methods significantly depends on the number of extracted key-points and the quality of their distribution. Therefore, it is important to examine the quality of distribution of key-points by using a suitable measure. However, in literature, examining distribution of key-points could not get appropriate attention. Very few metrics have been reported that discussed about the distribution quality of the feature points. These metrics consider all detected key-points inliers (correctly matched key-points). Therefore, they may provide reasonable distribution quality measure in the absence of outliers. However, in the presence of outliers, these measures are not compliance with accuracy and may provide misleading quantities. In this paper, we propose a distribution quality metric that can provide a reliable measure for distribution and overcomes the limitations in the existing measures. The proposed measure uses area and shape of Delaunay triangles and incorporates the goodness of the key-points. Experimental results show that the proposed measure can evaluate distribution quality accurately even in the presence of outliers. It is also well-compliance with the registration accuracy.
Object Detection by Combining Deep Dilated Convolutions Network and Light-Weight Network

In recent years, the performance of object detection algorithm has been improved continuously, and it has become an important direction in the field of computer vision. All the work in this paper will be based on a two-stage object detection algorithm. First, the dilated convolution network is added to the backbone network to form the Deep_Dilated Convolution Network (D_dNet), which improves the resolution of the feature map and the size of the receptive field. In addition, in order to obtain higher accuracy, the feature map of pretraining is compressed and a light-weight network is established. Finally, to further optimize the proposed two network models, this paper introduces the transfer learning into the pretraining. The whole experiment is evaluated based on MSCOCO dataset. Experiments show that the accuracy of the proposed model is improved by 1.3 to 2.2% points.
Retrieved Image Refinement by Bootstrap Outlier Test

Outlier detection is used to identify data points or a small number of subsets of data that are significantly different from most other data in a given dataset. It is challenging to detect outliers using an objective and quantitative approach. Methods that use the framework of statistical hypothesis testing are widely used by assuming a specific parametric distribution as a data generation model, but there is no guarantee that the distribution of data can be adequately approximated by a parametric distribution in practical problems. In this paper, a simple method is proposed to objectively detect outliers by hypothesis testing without assuming a specific distribution of outlier scores. By using an arbitrary outlier score function, hypothesis testing is used to determine whether each given sample is an outlier. The distribution of the test statistics is needed for the hypothesis test, and is estimated based on the given data using the bootstrap method. The effectiveness of the proposed outlier test was verified by applying it to outlier detection for text-based image retrieval, where it improved the quality of image searches by removing irrelevant images.
Natural Language Understanding in Smartdialog

Nowadays in the modern world, interactive smart dialogs with text or voice are gaining traction as the main digital interaction channel between human and machine. However, most of the current platforms do not support or have not fully developed for Vietnamese. In this paper, the authors propose a smart conversational platform through a text channel and/or voice channel in Vietnamese language, including these main steps: (i) Input Conversion and Pre-Processing, (ii) Entity Recognition, (iii) Intent Classification, (iv) Action Prediction and Execution, and (v) Output Generation. This paper focuses on presenting problems related to natural language understanding. To recognize entities in a sentence, the authors studied and optimized the features for Vietnamese with the Conditional Random Field model. With the problem of predicting user intent, this work proposed, experimented, and compared of Random Forest and BiLSTM deep learning model to optimize for the Vietnamese language. A platform was built and deployed for Milo smart speaker application (LUMI smart home) and VADI driver virtual assistant with the accuracy of around 98.7%.
Fin-Ray effect® based structure for the passive mobilization of the hand joints

In medical rehabilitation and physical therapy practice it has been proved that the passive mobilization of the hand joints as part of rehabilitation exercises improves the recovery period and also diminishes the rehabilitation costs by about 50%. In order to facilitate the practice of passive mobilization exercises in both medical units and patient homes the development of new adequate rehabilitation equipment has become a necessity. It is within the context of this requirement that the paper puts forward and discusses a novel solution of rehabilitation equipment that makes possible the simultaneous exercising of the radiocarpal, metacarpophalangeal and interphalangeal joints. The first novelty proposed by this equipment is using the Fin-Ray effect® in the construction of the hand support and mobilization structure. A further novelty is the utilization of a pneumatic muscle as the motor that drives the equipment. The paper presents the construction of the equipment and discusses range of motion generated by it compared to the corresponding motion limits of the human hand, as identified in literature.
Opponent Modeling Under Partial Observability in StarCraft with Deep Convolutional Encoder-Decoders

StarCraft, one of the most popular real-time strategy games, is a compelling environment for artificial intelligence research involving various tasks of both micro-level unit control and macro-level strategic decision making. In this study, we address an eminent problem of concern in macro-level decision making known as the “fog-of-war”, which rises from the partial observable nature of the game. Recovering information hidden under the fog can help capture advantageous high-level game dynamics, such as build orders, tactics and strategies of the opponent. Casted as a supervised learning problem, we propose a convolutional encoder-decoder architecture to predict potential counts and locations of the opponent’s units based on only partially visible and noisy state information. We visualize the model predictions on simplified grids to primarily evaluate the performance of our proposed method. Furthermore, we train an additional convolutional neural network classifier on the encoder-decoder outputs to predict the final winner of the game, as a means of demonstrating both effectiveness and applicability.
STCMH with minimal semantic loss

Cross-modal hashing (CMH) has received widespread attention due to high retrieval efficiency, which plays an extremely important role in cross-modal retrieval. Recently, many CMH methods have been proposed to establish the semantic connection of different modalities. However, most of these methods only use a simple quantisation strategy, resulting in large quantisation error, and inferior hash codes. To address this issue, in this study, the authors propose a novel self-taught CMH (STCMH) to minimise the semantic encoding loss. In particular, the common semantic representations across different modalities are first learnt based on collective matrix factorisation. Then, the quantisation procedure based on orthogonal transformation is integrated to encode the semantic representations into discriminative binary codes. Moreover, similarity preservation is imposed to further boost the discriminative power. Finally, hashing functions learning is formulated as a binary classification problem by self-taught scheme. Experimental results on three public datasets demonstrate that STCMH significantly outperforms most state-of-the-art CMH methods.
A Comparative Study of Pointing Techniques for Eyewear Using a Simulated Pedestrian Environment

Eyewear displays allow users to interact with virtual content displayed over real-world vision, in active situations like standing and walking. Pointing techniques for eyewear displays have been proposed, but their social acceptability, efficiency, and situation awareness remain to be assessed. Using a novel street-walking simulator, we conducted an empirical study of target acquisition while standing and walking under different levels of street crowdedness. We evaluated three phone-based eyewear pointing techniques: indirect touch on a touchscreen, and two in-air techniques using relative device rotations around forward and a downward axes. Direct touch on a phone, without eyewear, was used as a control condition. Results showed that indirect touch was the most efficient and socially acceptable technique, and that in-air pointing was inefficient when walking. Interestingly, the eyewear displays did not improve situation awareness compared to the control condition. We discuss implications for eyewear interaction design.
Training with Additional Semantic Constraints for Enhancing Neural Machine Translation

Replacing the traditional cross-entropy loss with BLEU as the optimization objective is a successful application of reinforcement learning (RL) in neural machine translation (NMT). However, a considerable weakness of the approach is that the monotonic optimization of BLEU’s training algorithm ignores the semantic fluency of the translation. One phenomenon is an incomprehensible translation accompanied by an ideal BLEU. In addition, sampling inefficiency as a common shortcoming of RL is more prominent in NMT. In this study, we address these issues in two ways. (1) We use the annealing schedule algorithm to add semantic evaluation for reinforcement training as part of the training objective. (2) We further attach a value iteration network to RL to transform the reward into a decision value, thereby making model training highly targeted and efficient. We use our approach on three representative language machine translation tasks, including low resource Mongolian-Chinese, agglutinative Japanese-English, and common task English-Chinese. Experiments show that our approach achieves significant improvements over the strong baselines, besides, it also saves nearly one-third of training time on different tasks.
Syntax-Aware Sentence Matching with Graph Convolutional Networks

Natural language sentence matching, as a fundamental technology for a variety of tasks, plays a key role in many natural language processing systems. In this article, we propose a new method which incorporates syntactic structure into “matching-aggregation” framework for sentence matching tasks. Our approach can be used in “matching-aggregation” framework efficiently. Concretely speaking, we introduce a multi-channel-GCN layer, which takes both words and the syntactic dependency trees of sentence pair as input to incorporate syntax information to the matching process. We also use a gating mechanism to dynamically combine the raw contextual representation of a sentence with the syntactic representation of the sentence to relieve the noise caused by the potential wrong dependency parsing result. Experimental results on standard benchmark datasets demonstrate that our model makes a substantial improvement over the baseline.
Preferential Tableaux for Contextual Defeasible $$\mathcal {ALC}$$

In recent work, we addressed an important limitation in previous extensions of description logics to represent defeasible knowledge, namely the restriction in the semantics of defeasible concept inclusion to a single preference order on objects of the domain. Syntactically, this limitation translates to a context-agnostic notion of defeasible subsumption, which is quite restrictive when it comes to modelling different nuances of defeasibility. Our point of departure in our recent proposal allows for different orderings on the interpretation of roles. This yields a notion of contextual defeasible subsumption, where the context is informed by a role. In the present paper, we extend this work to also provide a proof-theoretic counterpart and associated results. We define a (naïve) tableau-based algorithm for checking preferential consistency of contextual defeasible knowledge bases, a central piece in the definition of other forms of contextual defeasible reasoning over ontologies, notably contextual rational closure.
The Entropy Rate of Some Pólya String Models

We study random string-duplication systems, which we call Pólya string models. These are motivated by a class of mutations that are common in most organisms and lead to an abundance of repeated sequences in their genomes. Unlike previous works that study the combinatorial capacity of string-duplication systems, or in a probabilistic setting, various string statistics, this work provides the exact entropy rate or bounds on it, for several probabilistic models. The entropy rate determines the compressibility of the resulting sequences, as well as quantifying the amount of sequence diversity that these mutations can create. In particular, we study the entropy rate of noisy string-duplication systems, including the tandem-duplication, end-duplication, and interspersed-duplication systems, where in all cases we study duplication of length 1 only. Interesting connections are drawn between some systems and the signature of random permutations, as well as to the beta distribution common in population genetics.
Research on Subject Teaching Knowledge with Information Technology Deconstruction Perspective

This paper proposes the method of professional knowledge to affect all aspects of teacher education and teaching, corresponding to teachers' understanding of the curriculum and the role of textbooks.The methods also influence the choice and design of technology.Therefore, teachers should have a deep understanding of the reciprocity between this mechanism, which involves the relationship of roles that teachers need to know the existence form and basic composition of different technologies in the context of teaching and learning.Also, the subject teaching knowledge in the framework is an aggregation of content knowledge and pedagogy knowledge, in terms of the knowledge about courses, evaluations, and reports included in the subject teaching knowledge.In conclusion, we can have wisdom, flexibility, and tension to choose the appropriate teaching method for subject content, and support technology to promote the occurrence of learning and the deconstruction of intelligent behavior.
Comparison of Embedded and Wrapper Approaches for Feature Selection in Support Vector Machines

Feature selection methods are generally divided into three categories: filter, wrapper and embedded approaches. In terms of learning performance, the filter approach is typically inferior compared to the other two because it does not use the target learning algorithm. The embedded and wrapper approaches are both considered high-performing. In this paper we compare the embedded and the wrapper approaches in the context of Support Vector Machines (SVMs). In the wrapper category, we compare well-known algorithms such as Genetic Algorithm (GA), Forward and Backward selection, and a new binary Particle Swarm Optimization (PSO) algorithm. For an embedded approach we devise a new heuristic algorithm based on Multiple Kernel Learning.
Customizing Websites Through Automatic Web Search

The WorldWide Web has endured an incredible growth in the last decades. Nowadays, we can visit an unimaginable number of websites from different devices (laptops, mobiles, tablets...) in order to obtain immediate information. However, this information is separated in different resources and Web information search is unpleasant. The feeling of the users is frustrating when collecting information from different resources. Techniques such as Web personalization and Web customization have been an important research area during the last years. Web customization techniques have been widely used to develop website adaptation along the WorldWide Web. This customization is frequently performed by end-users that use the numerous tools available to carry out this assignment. This article presents Excore, a Web customization tool that permits end-users to customize their websites with automatic Web searches. The article presents the benefits introduced by Excore as a response to the drawbacks end-users experience while they perform their Web activities. Evaluation of the Web customization tool is also addressed in the paper. Evaluation is performed with stakeholders by means of tests and surveys. Results show that testers overcome the detected drawbacks with the use of Excore.
Fuzzy Controller for Sun Tracking (Using Image Processing)

The proposed paper discusses a new technique used for sun tracking systems, image processing and feature extraction used to determine the sun position. Then a fuzzy logic controller is used to control the photovoltaic (PV) tracking angle. At maximum intensity of light, the PV cells generate maximum power. The proposed system is designed and simulated with MATLAB as a simulation software. Image processing, signal processing, fuzzy logic and Simulink tools box are used for controller design. The simulated result matches with the proposed modules. What is presented in this paper is an approach aiming at minimizing the use of fossil fuel which is a source of environmental pollution and maximizing the use of renewable energy for more efficient power production by means of intensifying the sun’s radiation. This technique is a base for the development of models for sun tracking which are more sophisticated and more accurate the current techniques.
An Algebra of Modular Systems: Static and Dynamic Perspectives

We introduce static and dynamic algebras for specifying combinations of modules communicating among them via shared second-order variables. In the static algebra, atomic modules are classes of structures. They are composed using operations of extended Codd’s relational algebra, or, equivalently, first-order logic with least fixed point. The dynamic algebra has essentially the same syntax, but with a specification of inputs and outputs in addition. In the dynamic setting, atomic modules are formalized in any framework that allows for the specification of their input-output behaviour by means of model expansion. Algebraic expressions are interpreted by binary relations on structures. We demonstrate connections of the dynamic algebra with a modal temporal logic and deterministic while programs.
Interactive Video Annotation Tool for Generating Ground Truth Information

In this paper, we propose an interactive video and image annotation tool to generate ground truth information, that is essential information for training deep neural network. The proposed annotation tool not only generates various ground truth (GT) information such as object, motion, and event information, but also supports a semi-automatic video and image annotation method for fast generation of ground truth. The ground truth generated in the proposed tool is stored in the metadata database as a form of XML. The implementation results show that the proposed annotation tool provides faster and more detailed ground truth information compared to the existing methods.
FileCrypt: Transparent and Scalable Protection of Sensitive Data in Browser-based Cloud Storage

While cloud storage has become a common practice for more and more organizations, many severe cloud data breaches in recent years show that protecting sensitive data in the cloud is still a challenging problem. Although various mitigation techniques have been proposed, they are not scalable for large scale enterprise users with strict security requirements or often depend on error-prone human interventions. To address these issues, we propose FileCrypt, a generic proxy-based technique for enterprise users to automatically secure sensitive files in browser-based cloud storage. To the best of our knowledge, FileCrypt is the first attempt towards transparent and fully automated file encryption for browser-based cloud storage services. More importantly, it does not require active cooperations from cloud providers or modifications of existing cloud applications. By instrumenting mandatory file-related JavaScript APIs in browsers, FileCrypt can naturally support new cloud storage services and guarantee the file encryption cannot be bypassed. We have evaluated the efficacy of FileCrypt on a number of popular realworld cloud storage services. The results show that it can protect files on the public cloud with relatively low overheads.