Reviewerly/exHarmony huggingface dataset:
Monte Carlo Tree Search-Based Recursive Algorithm for Feature Selection in High-Dimensional Datasets

The complexity and high dimensionality are the inherent concerns of big data. The role of feature selection has gained prime importance to cope with the issue by reducing dimensionality of datasets. The compromise between the maximum classification accuracy and the minimum dimensions is as yet an unsolved puzzle. Recently, Monte Carlo Tree Search (MCTS)-based techniques have been invented that have attained great success in feature selection by constructing a binary feature selection tree and efficiently focusing on the most valuable features in the features space. However, one challenging problem associated with such approaches is a tradeoff between the tree search and the number of simulations. In a limited number of simulations, the tree might not meet the sufficient depth, thus inducing biasness towards randomness in feature subset selection. In this paper, a new algorithm for feature selection is proposed where multiple feature selection trees are built iteratively in a recursive fashion. The state space of every successor feature selection tree is less than its predecessor, thus increasing the impact of tree search in selecting best features, keeping the MCTS simulations fixed. In this study, experiments are performed on 16 benchmark datasets for validation purposes. We also compare the performance with state-of-the-art methods in literature both in terms of classification accuracy and the feature selection ratio.
Edge-Guided Image Downscaling With Adaptive Filtering

In this paper, we propose a novel framework for image downscaling with edge-guided interpolation and adaptive filtering. First, we extract the second derivative edge-guidance map from an input image. Then, inter-pixels are interpolated via edge-guidance map and portion of distance from the input pixels. Finally, adaptive filtering is applied to the expanded pixels for alleviating artifacts while preserving details and contents of input image. Experimental results validate the proposed framework can achieve content-preserving results while reducing artifacts.
Adoption of Hybrid methodology in projects

Agile project management approach is known for its adaptability to changes in project lifecycle but has uncertainties in costs and time. Debates are still ongoing between practitioners and the acad ...
A Hierarchical Vehicular-Based Architecture for Vehicular Networks: A Case Study on Computation Offloading

In order to realize an intelligent transportation system (ITS) which will provide smooth urban traffic, autonomous driving, accurate route navigation, etc., enormous computations need to be migrated from cloud centers to edge nodes, especially for the services requiring stringent latency. In addition to base stations and road side units (RSUs), vehicles can be alteratively considered as a kind of computation resources. In this article, a hierarchical vehicular-based architecture which consists of cloud centers and vehicles is investigated. Computation offloading performance in the hierarchical architecture is also studied. In specific, the main components in vehicular networks and their characteristics on communication and computations are presented firstly. Several communication techniques that are essential in enabling computation offloading among these components are then discussed. Secondly, a hierarchical vehicular-based architecture, which integrates the main components, is constructed. Thirdly, a case study on computation offloading in the proposed architecture is conducted. In the concerned scenario, the computation offloading problem is modelled as a multi-dimensional multiple knapsack problem (MMKP). Two algorithms are investigated, among which, the first algorithm is a greedy heuristic method providing a sub-optimal solution with a low computational complexity. The second algorithm is a modified branch and bound (B&B) method, which can obtain the best solution with a high computational complexity. Numerical results are also presented to verify the performance of the two algorithms. It can be demonstrated that the proposed architecture can migrate more computations from cloud centers to vehicular nodes, when the computations require more communication resources.
The Role of Playing Model Learning Based on Tri Hita Karana Improve Indonesian Language Learning Outcomes

This study aims to determine the effect of the Tri Hita Karana-based Role-Playing type of cooperative learning model on Indonesian language learning outcomes. This research is a quasi-experimental study with a non-equivalent post-test only control group design. The population of this study was 177 people, the sample was determined using simple random sampling. The sample of this research was 28 students in class IV SD 1 Banjar Tegal and 28 students in class IV SD 1 Baktiseraga as many as 28 people. Indonesian learning outcomes data obtained through the method of providing multiple choice tests. Based on the results of hypothesis testing shows that there are significant differences in Indonesian learning outcomes between groups of students who are taught with a cooperative learning model based on Tri Hita Karana type and students who are not taught using the cooperative learning model based on Tri Hita Karana. Thus, the cooperative learning model of the type of Role Playing based on Tri Hita Karana because it affects the learning outcomes of Indonesian students. Based on the results of the study, it is recommended that this study can be used as a support or relevant study in subsequent studies to fund a type of cooperative learning model based on Tri Hita Karana.
LMA: label-based multi-head attentive model for long-tail web service classification

With the rapid growth of web services, service classification is widely used to facilitate service discovery, selection, composition and recommendation. Although there is much research in service classification, work rarely focuses on the long-tail problem to improve the accuracy of those categories which have fewer services. In this paper, we propose a novel label-based attentive model LMA with the multi-head structure for long-tail service classification. It can learn the various word-label subspace attention with a multi-head mechanism, and concatenate them to get the high-level feature of services. To demonstrate the effectiveness of LMA, extensive experiments are conducted on 14,616 real-world services with 80 categories crawled from the service repository ProgrammableWeb. The results prove that the LMA outperforms state-of-the-art approaches for long-tail service classification in terms of multiple evaluation metrics.
Maximum dimension of subspaces with no product basis

Let n≥2 and d1,…,dn≥2 be integers, and F be a field. A vector u∈Fd1⊗⋯⊗Fdn is called a product vector if u=u[1]⊗⋯⊗u[n] for some u[1]∈Fd1,…,u[n]∈Fdn. A basis composed of product vectors is called a product basis. In this paper, we show that the maximum dimension of subspaces of Fd1⊗⋯⊗Fdn with no product basis is equal to d1d2⋯dn−2 if either (i) n=2 or (ii) n≥3 and #F>max⁡{di:i≠n1,n2} for some n1 and n2. When F=C, this result is related to the maximum number of simultaneously distinguishable states in general probabilistic theories (GPTs).
Formation of value-semantic guidelines for future specialists in the conditions of functioning of the digital environment

The article reveals the relevance of studying the problems of formation of value-semantic guidelines for future specialists who study in the digital environment of educational organizations. The role of the digital environment in training competent specialists is noted. The theoretical basis of the article is the current approaches in teaching, highlighted on the basis of the analysis of psychological and pedagogical literature, research on the formation of value and semantic orientations of future specialists. The article reveals the principles of organization and conduct of educational work of a teacher of an educational organization that ensure the formation of value-semantic orientations of a person receiving a profession. The need for the teacher in the basics of social psychology, the knowledge in the field of theory and methods of professional education is justified. The author reveals and describes the role of a teacher in conducting educational work in order to successfully establish value and semantic guidelines for future specialists in the digital environment of an educational organization.
A Novel Chaotic System for Text Encryption Optimized with Genetic Algorithm

With meteoric developments in communication systems and data storage technologies, the need for secure data transmission is more crucial than ever.The level of security provided by any cryptosystem relies on the sensitivity of the private key, size of the key space as well as the trapdoor function being used.In order to satisfy the aforementioned constraints, there has been a growing interest over the past few years, in studying the behavior of chaotic systems and their applications in various fields such as data encryption due to characteristics like randomness, unpredictability and sensitivity of the generated sequence to the initial value and its parameters.This paper utilizes a novel 2D chaotic function that displays a uniform bifurcation over a large range of parameters and exhibits high levels of chaotic behavior to generate a random sequence that is used to encrypt the input data.The proposed method uses a genetic algorithm to optimize the parameters of the map to enhance security for any given textual data.Various analyses demonstrate an adequately large key space and the existence of multiple global optima indicating the necessity of the proposed system and the security provided by it.
Experiencing Real-time 3D Interaction with Depth Maps for Mobile Augmented Reality in DepthLab

We demonstrate DepthLab, a playground for interactive augmented reality experiences leveraging the shape and depth of the physical environment on a mobile phone. Based on the ARCore Depth API, DepthLab encapsulates a variety of depth-based UI/UX paradigms, including geometry-aware rendering (occlusion, shadows, texture decals), surface interaction behaviors (physics, collision detection, avatar path planning), and visual effects (relighting, 3D-anchored focus and aperture effects, 3D photos). We have open-sourced our software at https://github.com/googlesamples/arcore-depth-lab to facilitate future research and development in depth-aware mobile AR experiences. With DepthLab, we aim to help mobile developers to effortlessly integrate depth into their AR experiences and amplify the expression of their creative vision.
Automatic generation of Chinese abstract based on vocabulary and LSTM neural network

Most methods of Chinese short text summarisation are based on extraction, and it's hard to guarantee that the abstract is consistent. In this paper, we present an effective automatic method of Chinese abstract by using vocabulary and long-short term memory neural networks. The method utilises the seq2seq architecture, and introduces the candidate vocabulary in the decoding stage, to reduce the decoder vocabulary size. Thus, the training process is faster and the result is more concise and grammatical. In the end, experimental results validate the correctness and effectiveness of the method by taking a Large-Scale Chinese Short Text Summarisation (LCSTS) data set and Recall-Oriented Understudy for Gisting Evaluation (ROUGE).
Boosting Knowledge Base Automatically via Few-Shot Relation Classification

Relation classification (RC) aims at extracting structural information, i.e, triplets of two entities with a relation, from free texts, which is pivotal for automatic knowledge base construction. In this paper, we investigate a fully automatic method to train a RC model which facilitates to boost the knowledge base. Traditional RC models cannot extract new relations unseen during training since they define RC as a multiclass classification problem. The recent development of few-shot learning (FSL) provides a feasible way to accommodate to fresh relation types with a handful of examples. However, it requires a moderately large amount of training data to learn a promising few-shot RC model, which consumes expensive human labor. This issue recalls a kind of weak supervision methods, dubbed distant supervision (DS), which can generate the training data automatically. To this end, we propose to investigate the task of few-shot relation classification under distant supervision. As DS naturally brings in mislabeled training instances, to alleviate the negative impact, we incorporate various multiple instance learning methods into the classic prototypical networks, which can achieve sentence-level noise reduction. In experiments, we evaluate our proposed model under the standard N-way K-shot setting of few-shot learning. The experiment results show that our proposal achieves better performance.
Crowdfunding Using Blockchain

Crowd funding is the practice of raising money from large number of individuals for the purpose of financing a project, venture, business or cause. Conventionally, crowdfunding has been carried out via events and door-to door fundraising. However there are lot of risk features like security of your business idea, developing a successful crowd funding campaign is an effort and time consuming. Crowd funding is not a charity, fees charged, risk of failure etc. Here comes the role of blockchain to nullify the potential risks of the conventional method of fundraising. A decentralized approach to crowd funding allows us to eliminate all the potential risks faced by the conventional approach of crowdfunding.
Experimental characterization of quantum processes: A selective and efficient method in arbitrary finite dimensions

The temporal evolution of a quantum system can be characterized by quantum process tomography, a complex task that consumes a number of physical resources scaling exponentially with the number of subsystems. An alternative approach to the full reconstruction of a quantum channel allows selecting which coefficient from its matrix description to measure, and how accurately, reducing the amount of resources to be polynomial. The possibility of implementing this method is closely related to the possibility of building a complete set of mutually unbiased bases (MUBs) whose existence is known only when the dimension of the Hilbert space is the power of a prime number. However, an extension of the method that uses tensor products of maximal sets of MUBs, has been introduced recently. Here we explicitly describe how to implement this algorithm to selectively and efficiently estimate any parameter characterizing a quantum process in a non-prime power dimension, and we conducted for the first time an experimental verification of the method in a Hilbert space of dimension $d=6$. That is the small space for which there is no known a complete set of MUBs but it can be decomposed as a tensor product of two other Hilbert spaces of dimensions $D_1=2$ and $D_2=3$, for which a complete set of MUBs is known. The $6$-dimensional states were codified in the discretized transverse momentum of the photon wavefront. The state preparation and detection stages are dynamically programmed with the use of only-phase spatial light modulators, in a versatile experimental setup that allows to implement the algorithm in any finite dimension.
Sparse Data Interpolation Using the Geodesic Distance Affinity Space

In this letter, we adapt the geodesic distance-based recursive filter to the sparse data interpolation problem. The proposed technique is general and can be easily applied to any kind of sparse data. We demonstrate its superiority over other interpolation techniques in three experiments for qualitative and quantitative evaluation. In addition, we compare our method with the popular interpolation algorithm presented in the paper on EpicFlow optical flow, which is intuitively motivated by a similar geodesic distance principle. The comparison shows that our algorithm is more accurate and considerably faster than the EpicFlow interpolation technique.
The Extended Theory of Trees and Algebraic (Co)datatypes

The first-order theory of finite and infinite trees has been studied since the eighties, especially by the logic programming community.Following Djelloul, Dao and Frühwirth, we consider an extension of this theory with an additional predicate for finiteness of trees, which is useful for expressing properties about (not just datatypes but also) codatatypes.Based on their work, we present a simplification procedure that determines whether any given (not necessarily closed) formula is satisfiable, returning a simplified formula which enables one to read off all possible models.Our extension makes the algorithm usable for algebraic (co)datatypes, which was impossible in their original work due to restrictive assumptions.We also provide a prototype implementation of our simplification procedure and evaluate it on instances from the SMT-LIB.
Generative Adversarial Networks for Improving Object Detection in Camouflaged Images

The effectiveness of object detection largely depends on the availability of large annotated datasets to train the deep network successfully; however, obtaining a large-scale dataset is expensive and remains a challenge. In this work, we explore two different GAN-based approaches for data augmentation of agricultural images in a camouflaged environment. Camouflage is the property of an object which makes it hard to detect because of its similarity to its environment. We leverage paired and unpaired image-to-image translation to create synthetic images based on custom segmentation masks. We evaluate the quality of synthetic images by applying these to the object detection task as additional training samples. The experiments demonstrate that adversarial-based data augmentation significantly improves the accuracy of region-based convolutional neural network for object detection. Our findings show that when evaluated on the testing dataset, data augmentation achieves detection performance improvement of 3.97%. Given the difficulty of object detection task in camouflaged images, the result suggests that combining adversarial-based data augmentation with the original data can theoretically be synergistic in enhancing deep neural network efficiency to address the open problem of detecting objects in camouflaged environments.
Individual attacks with generalized discrimination and inadequacy of some information measures

We accomplish studies of properly quantifying eavesdropper's information gain in individual attacks on the BB84 system of quantum key distribution. A noticeable sensitivity of conclusions to the choice of information measures is brightly revealed, when generalized state discrimination is used at the last stage. To realize her aims, Eve intrudes into the communication channel with some entangling probe. The Fuchs-Peres-Brandt (FPB) probe is known as a powerful individual attack on the BB84 protocol. In the simplest formulation, Eve uses the Helstrom scheme to distinguish output probe states. In conclusive eavesdropping, the unambiguous discrimination is utilized. In the intermediate scenario, she can apply a generalized discrimination scheme that interpolates between the Helstrom and unambiguous discrimination schemes. We analyze Eve's probe performance from the viewpoint of various measures for quantifying mutual information.
Extracting Body Text from Academic PDF Documents for Text Mining

Accurate extraction of body text from PDF-formatted academic documents is essential in text-mining applications for deeper semantic understandings. The objective is to extract complete sentences in the body text into a txt file with the original sentence flow and paragraph boundaries. Existing tools for extracting text from PDF documents would often mix body and nonbody texts. We devise and implement a system called PDFBoT to detect multiple-column layouts using a line-sweeping technique, remove nonbody text using computed text features and syntactic tagging in backward traversal, and align the remaining text back to sentences and paragraphs. We show that PDFBoT is highly accurate with average F1 scores of, respectively, 0.99 on extracting sentences, 0.96 on extracting paragraphs, and 0.98 on removing text on tables, figures, and charts over a corpus of PDF documents randomly selected from arXiv.org across multiple academic disciplines.
Fine-Grained Dynamic Head for Object Detection

The Feature Pyramid Network (FPN) presents a remarkable approach to alleviate the scale variance in object representation by performing instance-level assignments. Nevertheless, this strategy ignores the distinct characteristics of different sub-regions in an instance. To this end, we propose a fine-grained dynamic head to conditionally select a pixel-level combination of FPN features from different scales for each instance, which further releases the ability of multi-scale feature representation. Moreover, we design a spatial gate with the new activation function to reduce computational complexity dramatically through spatially sparse convolutions. Extensive experiments demonstrate the effectiveness and efficiency of the proposed method on several state-of-the-art detection benchmarks. Code is available at this https URL.
Modular Constraint Solver Cooperation via Abstract Interpretation

Cooperation among constraint solvers is difficult because different solving paradigms have different theoretical foundations. Recent works have shown that abstract interpretation can provide a unifying theory for various constraint solvers. In particular, it relies on abstract domains which capture constraint languages as ordered structures. The key insight of this paper is viewing cooperation schemes as abstract domains combinations. We propose a modular framework in which solvers and cooperation schemes can be seamlessly added and combined. This differs from existing approaches such as SMT where the cooperation scheme is usually fixed (e.g., Nelson-Oppen). We contribute to two new cooperation schemes: (i) interval propagators completion that allows abstract domains to exchange bound constraints, and (ii) delayed product which exchanges over-approximations of constraints between two abstract domains. Moreover, the delayed product is based on delayed goal of logic programming, and it shows that abstract domains can also capture control aspects of constraint solving. Finally, to achieve modularity, we propose the shared product to combine abstract domains and cooperation schemes. Our approach has been fully implemented, and we provide various examples on the flexible job shop scheduling problem. Under consideration for acceptance in TPLP.
Decrement Operators in Belief Change

While research on iterated revision is predominant in the field of iterated belief change, the class of iterated contraction operators received more attention in recent years. In this article, we examine a non-prioritized generalisation of iterated contraction. In particular, the class of weak operators is introduced, which are operators that by multiple steps achieve the same as a contraction. Inspired by Darwiche and Pearl’s work on iterated revision the subclass of operators is defined. For both, decrement and weak decrement operators, postulates are presented and for each of them a representation theorem in the framework of total preorders is given. Furthermore, we present two sub-types of operators.
Governing the Internet of Things

Beginning in 2020, smart devices sold in California that connect “directly or indirectly” to the public Internet have to be equipped with “reasonable” cybersecurity features such as a unique password.<sup>1</sup> But how, exactly, should we define “reasonable” security? More broadly, are we...