Reviewerly/exHarmony huggingface dataset:
HDDL -- A Language to Describe Hierarchical Planning Problems

The research in hierarchical planning has made considerable progress in the last few years. Many recent systems do not rely on hand-tailored advice anymore to find solutions, but are supposed to be domain-independent systems that come with sophisticated solving techniques. In principle, this development would make the comparison between systems easier (because the domains are not tailored to a single system anymore) and -- much more important -- also the integration into other systems, because the modeling process is less tedious (due to the lack of advice) and there is no (or less) commitment to a certain planning system the model is created for. However, these advantages are destroyed by the lack of a common input language and feature set supported by the different systems. In this paper, we propose an extension to PDDL, the description language used in non-hierarchical planning, to the needs of hierarchical planning systems. We restrict our language to a basic feature set shared by many recent systems, give an extension of PDDL's EBNF syntax definition, and discuss our extensions with respect to several planner-specific input languages from related work.
Improved Person Detection on Omnidirectional Images with Non-maxima Supression

We propose a person detector on omnidirectional images, an accurate method to generate minimal enclosing rectangles of persons. The basic idea is to adapt the qualitative detection performance of a convolutional neural network based method, namely YOLOv2 to fish-eye images. The design of our approach picks up the idea of a state-of-the-art object detector and highly overlapping areas of images with their regions of interests. This overlap reduces the number of false negatives. Based on the raw bounding boxes of the detector we fine-tuned overlapping bounding boxes by three approaches: the non-maximum suppression, the soft non-maximum suppression and the soft non-maximum suppression with Gaussian smoothing. The evaluation was done on the PIROPO database and an own annotated Flat dataset, supplemented with bounding boxes on omnidirectional images. We achieve an average precision of 64.4 % with YOLOv2 for the class person on PIROPO and 77.6 % on Flat. For this purpose we fine-tuned the soft non-maximum suppression with Gaussian smoothing.
Building Strong Library and Information Science Education: IFLA Working Group White Paper (Expert Review)

The Building Strong Library and Information Science Education (BSLISE) Working Group of the International Federation of Library Associations and Institutions (IFLA) was formed in 2016 to develop an international quality assurance (QA) framework that will guide and promote international educational standards in LIS. The first phase of its efforts has been to conduct an international survey (by means of questionnaires) of qualifications for LIS professional practice inclusive of regional and national contexts. Key findings of the study along with recommendations appear in the Building Strong Library and Information Science Education: a Call to Global and Local Action White Paper published in August 2018. This review briefly outlines the White Paper’s content and comments on its main points. Based on the analysis, the BSLISE Working Group identified six key findings with recommended actions and change agents, and prioritized three actions for its next steps: to develop an international resource that identifies local structures; to develop an international framework for the assessment of quality standards in LIS education; to identify core and other competencies for transferability and reciprocity. The article concludes that there has been a longstanding need for comprehensive documentation of LIS education globally.
Cryptanalysis of Merkle-Hellman Cipher Using Parallel Genetic Algorithm

In 1976, Whitfield Diffie and Martin Hellman introduced the public key cryptography or asymmetric cryptography standards. Two years later, an asymmetric cryptosystem was published by Ralph Merkle and Martin Hellman called $ \mathcal {M} \mathcal {H} $, based on a variant of knapsack problem known as the subset-sum problem which is proven to be NP -hard. Furthermore, over the last four decades, Metaheuristics have achieved remarkable progress in solving NP-hard optimization problems. However, the conception of these methods raises several challenges, mainly the adaptation and the parameters setting. In this paper, we propose a Parallel Genetic Algorithm (PGA) adapted to explore effectively the search space of considerable size in order to break the $ \mathcal {M} \mathcal {H} $ cipher. Experimental study is included, showing the performance of the proposed attacking scheme, and finally a concluding comparison with lattice reduction attacks.
Anti-unification in Constraint Logic Programming

Abstract Anti-unification refers to the process of generalizing two (or more) goals into a single, more general, goal that captures some of the structure that is common to all initial goals. In general one is typically interested in computing what is often called a most specific generalization, that is a generalization that captures a maximal amount of shared structure. In this work we address the problem of anti-unification in CLP, where goals can be seen as unordered sets of atoms and/or constraints. We show that while the concept of a most specific generalization can easily be defined in this context, computing it becomes an NP-complete problem. We subsequently introduce a generalization algorithm that computes a well-defined abstraction whose computation can be bound to a polynomial execution time. Initial experiments show that even a naive implementation of our algorithm produces acceptable generalizations in an efficient way.
Determination of the Yield Radius and Yield Stress in 2198-T3 Aluminum Alloy by Means of the Dual-Scale Instrumented Indentation Test

A new dual-scale instrumented indentation test (DualS-IIT) methodology is here proposed to determine the yield stress (in a tensile-like sense). The methodology involves measuring the bulk yield radius, as defined by the expansion cavity model (ECM), induced by a Vickers macro-indentation in a medium plane cross-section. The bulk yield radius is measured by means of a subsurface nano-hardness survey along the load direction in correspondence to the transition interface between the hemispherical strain hardened region and the surrounding elastic region. The methodology has been applied to an Al–Li (A2198-T3) alloy and a yield stress of 297 MPa has been measured (in agreement with a tensile test); moreover, anomalous plastic deformation behaviour has appeared under indentation. The combination of macro- and nano-indentation tests in one single experiment, as in the proposed methodology, offers a unique experimental basis to directly correlate the mechanical properties of a material at two different scales, which at present is an open issue in the research on indentation.
FACE RECOGNITION IMPLEMENTATION ON RASPBERRYPI USING OPENCV AND PYTHON

Distinguishing an individual with a picture has been advanced through the broad communications.Be that as it may, it is less powerful to unique finger impression or retina examining.This report depicts the face detection and recognition smaller than normal task attempted for the visual observation and self-governance module at Plymouth college.It reports the innovations accessible in the Open-Computer-Vision (OpenCV) library and technique to execute them utilizing Python.For face identification, Haar-Cascades were utilized and for face recognition Eigenfaces, Fisherfaces and Local double example histograms were utilized.The procedure is portrayed including stream diagrams for each phase of the framework.Next, the outcomes are indicated including plots and screen-shots pursued by an exchange of experienced difficulties.The report is finished up with the creators' feeling on the venture and potential applications.This paper means to execute a face recognition programming code dependent on the strategy for Haar Cascade Classifiers and to effectively actualize this code on the Raspberry Pi stage for continuous recognition.In this paper, an endeavor to execute face acknowledgment calculation on an equipment stage, which is basic, yet productive in utilization is taken up .The product source codes for both detection and recognition of countenances are composed utilizing Opencv and Python.
Efficient Genomic Interval Queries Using Augmented Range Trees

Abstract Efficient large-scale annotation of genomic intervals is essential for personal genome interpretation in the realm of precision medicine. There are 13 possible relations between two intervals according to Allen’s interval algebra. Conventional interval trees are routinely used to identify the genomic intervals satisfying a coarse relation with a query interval, but cannot support efficient query for more refined relations such as all Allen’s relations. We design and implement a novel approach to address this unmet need. Through rewriting Allen’s interval relations, we transform an interval query to a range query, then adapt and utilize the range trees for querying. We implement two types of range trees: a basic 2-dimensional range tree (2D-RT) and an augmented range tree with fractional cascading (RTFC) and compare them with the conventional interval tree (IT). Theoretical analysis shows that RTFC can achieve the best time complexity for interval queries regarding all Allen’s relations among the three trees. We also perform comparative experiments on the efficiency of RTFC, 2D-RT and IT in querying noncoding element annotations in a large collection of personal genomes. Our experimental results show that 2D-RT is more efficient than IT for interval queries regarding most of Allen’s relations, RTFC is even more efficient than 2D-RT. The results demonstrate that RTFC is an efficient data structure for querying large-scale datasets regarding Allen’s relations between genomic intervals, such as those required by interpreting genome-wide variation in large populations.
Bayesian Bootstraps for Massive Data

In this article, we present data-subsetting algorithms that allow for the approximate and scalable implementation of the Bayesian bootstrap.They are analogous to two existing algorithms in the frequentist literature: the bag of little bootstraps (Kleiner et al., 2014) and the subsampled double bootstrap (Sengupta et al., 2016).Our algorithms have appealing theoretical and computational properties that are comparable to those of their frequentist counterparts.Additionally, we provide a strategy for performing lossless inference for a class of functionals of the Bayesian bootstrap and briefly introduce extensions to the Dirichlet Process.
Noise reduction with image inpainting

For cytology, pathology or histology image analysis, whether performed by computer-aided algorithms or human experts, a general issue is to exclude the disturbance caused by noisy objects, especially when appeared with high similarities in shape, color and texture with target cell or tissues. In this paper, we introduce a novel model to reduce such type of noisy objects with large quantity and distribution in the microscope images based on deep learning and hand-craft features. The model experimentally reduces the false positives without effect on objects of interest for cancer detection. Moreover, it also provides much distinct images for human experts for the final diagnosis.
Comparative Analysis Run-Length Encoding Algorithm and Fibonacci Code Algorithm on Image Compression

Abstract Compression purpose to reduce the redundancy data as small as possible and speed up the data transmission process. To solve the size problem in saving data and transmission process, we use Run Length Encoding and Fibonacci Code algorithm to do compression process. Run Length Encoding and Fibonacci Code algorithm is a type of lossless data compression used in this research, which performance will be measured by comparison parameters of the Compression Ratio (CR), Redundancy (RD), Space Saving (SS) and Compression Time. The compression process is only done on image files with Bitmap format (*.bmp) and encode using Run Length Encoding or Fibonacci Code, then perform the compression process. The final result of the compression is file with extension *.rle or *.fib which contains compressed information that can be decompressed back. The output of the decompression result is an original image file that is stored with *.bmp extension. Fibonacci algorithm will give a better compressed size on image color, while in a grayscale image Run Length Encoding will give a better compressed size. Based on the results of research at two different types of images, each algorithm has its own advantages. Fibonacci Code algorithm is better for color image compression while Run-Length algorithm Encoding is better for grayscale image compression.
Hypergraph Induced Convolutional Manifold Networks

Deep convolutional neural networks (DCNN) with manifold embedding have achieved considerable attention in computer vision. However, prior arts are usually based on the neighborhood-based graph modeling only the pairwise relationship between two samples, which fail to fully capture intra-class variations and thus suffer from severe performance loss for noisy data. While such intra-class variations can be well captured via sophisticated hypergraph structure, we are motivated and lead a hypergraph induced Convolutional Manifold Network (H-CMN) to significantly improve the representation capacity of DCNN for the complex data. Specifically, two innovative designs are provides: 1) our manifold preserving method is implemented based on a mini-batch, which can be efficiently plugged into the existing DCNN training pipelines and be scalable for large datasets; 2) a robust hypergraph is built for each mini-batch, which not only offers a strong robustness against typical noise, but also captures the variances from multiple features. Extensive experiments on the image classification task on large benchmarking datasets demonstrate that our model achieves much better performance than the state-of-the-art
Distributed Meta-Brokering P2P Overlay for Scheduling in Cloud Federation

The interconnected cloud (Intercloud) federation is an emerging paradigm that revolutionizes the scalable service provision of geographically distributed resources. Large-scale distributed resources require well-coordinated and automated frameworks to facilitate service provision in a seamless and systematic manner. Unquestionably, standalone service providers must communicate and federate their cloud sites with other vendors to enable the infinite pooling of resources. The pooling of these resources provides uninterpretable services to increasingly growing cloud users more efficiently, and ensures an improved Service Level Agreement (SLA). However, the research of Intercloud resource management is in its infancy. Therefore, standard interfaces, protocols, and uniform architectural components need to be developed for seamless interaction among federated clouds. In this study, we propose a distributed meta-brokering-enabled scheduling framework for provision of user application services in the federated cloud environment. Modularized architecture of the proposed system with uniform configuration in participating resource sites orchestrate the critical operations of resource management effectively, and form the federation schema. Overlaid meta-brokering instances are implemented on the top of local resource brokers to keep the global functionality isolated. These instances in overlay topology communicate in a P2P manner to maintain decentralization, high scalability, and load manageability. The proposed framework has been implemented and evaluated by extending the Java-based CloudSim 3.0.3 simulation application programming interfaces (APIs). The presented results validate the proposed model and its efficiency to facilitate user application execution with the desired QoS parameters.
A Strategy Game System for QoS-Efficient Dynamic Virtual Machine Consolidation in Data Centers

In this paper, we develop a model for the competitive relationship between cloud service providers and cloud brokers during the virtual machine (VM) placement. The model adopts the idea of a strategy game, in which the cloud service provider and all VMs are participants. First, we define the strategy for the participants in the strategy game model according to the difference between the number of physical resources provided by the provider, and that requested for the VMs by the brokers. Next, payment values between the providers and brokers are obtained from each strategy in the game matrix, which corresponds to the strategy set of VM placement. In addition, the Nash equilibrium solution of the current strategy game demonstrates the trade-off between reducing energy consumption and guaranteeing QoS. On the basis of this, the optimal VM placement scheme is obtained, and the ongoing VM consolidation is examined. Finally, a strategy-game-based VM consolidation (SG-VMC) method is proposed. The extensive experimental results show that the proposed SG-VMC method can significantly reduce energy consumption while guaranteeing QoS.
Smart Technology Integration in the System of Bachelors’ Language Training

This paper considers the problem of smart technology integration into the system of bachelors’ language training at higher school environment. The use of smart technology in education helps to enrich traditional teaching methods and bring positive learning outcome. However, the application of smart technologies is connected with challenges both educators and students can face. The aim of the article is to consider and assess the use of smart technologies implemented into a foreign language teaching process. For that purpose, we applied such methods as a needs analysis (among lecturers of English from South Ural State University), a questionnaire and an interview (among three groups of students from similar bachelor’s program). The findings show that the majority of those surveyed do understand the importance of smart technology for professional education, but they are not familiar with the types of smart technologies and how to use them. Based on the results of this analysis, the authors applied smart technologies in the process of English teaching, such as different language learning platforms (Ed-modo, TEDed), social networks (VKontakte, Facebook), and a new online language development course, “English for General Purposes” which was implemented through the Moodle (the university corporate learning management system). The analyzed results and the drawn conclusions indicate that the integration of smart technologies allows to boost foreign language skills and encourage motivation.
Reducing Metadata Leakage from Encrypted Files and Communication with PURBs

Abstract Most encrypted data formats leak metadata via their plaintext headers, such as format version, encryption schemes used, number of recipients who can decrypt the data, and even the recipients’ identities. This leakage can pose security and privacy risks to users, e.g., by revealing the full membership of a group of collaborators from a single encrypted e-mail, or by enabling an eavesdropper to fingerprint the precise encryption software version and configuration the sender used. We propose that future encrypted data formats improve security and privacy hygiene by producing Padded Uniform Random Blobs or PURBs: ciphertexts indistinguishable from random bit strings to anyone without a decryption key. A PURB’s content leaks nothing at all , even the application that created it, and is padded such that even its length leaks as little as possible. Encoding and decoding ciphertexts with no cleartext markers presents efficiency challenges, however. We present cryptographically agile encodings enabling legitimate recipients to decrypt a PURB efficiently, even when encrypted for any number of recipients’ public keys and/or passwords, and when these public keys are from different cryptographic suites. PURBs employ P admé , a novel padding scheme that limits information leakage via ciphertexts of maximum length M to a practical optimum of O (log log M ) bits, comparable to padding to a power of two, but with lower overhead of at most 12% and decreasing with larger payloads.
Tree-based mining contrast subspace

All existing mining contrast subspace methods employ density-based likelihood contrast scoring function to measure the likelihood of a query object to a target class against other class in a subspace. However, the density tends to decrease when the dimensionality of subspaces increases causes its bounds to identify inaccurate contrast subspaces for the given query object. This paper proposes a novel contrast subspace mining method that employs tree-based likelihood contrast scoring function which is not affected by the dimensionality of subspaces. The tree-based scoring measure recursively binary partitions the subspace space in the way that objects belong to the target class are grouped together and separated from objects belonging to other class. In contrast subspace, the query object should be in a group having a higher number of objects of the target class than other class. It incorporates the feature selection approach to find a subset of one-dimensional subspaces with high likelihood contrast score with respect to the query object. Therefore, the contrast subspaces are then searched through the selected subset of one-dimensional subspaces. An experiment is conducted to evaluate the effectiveness of the tree-based method in terms of classification accuracy. The experiment results show that the proposed method has higher classification accuracy and outperform the existing method on several real-world data sets.
HadaNets: Flexible Quantization Strategies for Neural Networks

On-board processing elements on UAVs are currently inadequate for training and inference of Deep Neural Networks. This is largely due to the energy consumption of memory accesses in such a network. HadaNets introduce a flexible train-from-scratch tensor quantization scheme by pairing a full precision tensor to a binary tensor in the form of a Hadamard product. Unlike wider reduced precision neural network models, we preserve the train-time parameter count, thus out-performing XNOR-Nets without a train-time memory penalty. Such training routines could see great utility in semi-supervised online learning tasks. Our method also offers advantages in model compression, as we reduce the model size of ResNet-18 by 7.43× with respect to a full precision model without utilizing any other compression techniques. We also demonstrate a 'Hadamard Binary Matrix Multiply' kernel, which delivers a 10-fold increase in performance over full precision matrix multiplication with a similarly optimized kernel.
Swell-and-Shrink: Decomposing Image Captioning by Transformation and Summarization

Image captioning is currently viewed as a problem analogous to machine translation. However, it always suffers from poor interpretability, coarse or even incorrect descriptions on regional details. Moreover, information abstraction and compression, as essential characteristics of captioning, are always overlooked and seldom discussed. To overcome the shortcomings, a swell-shrink method is proposed to redefine image captioning as a compositional task which consists of two separated modules: modality transformation and text compression. The former is guaranteed to accurately transform adequate visual content into textual form while the latter consists of a hierarchical LSTM which particularly emphasizes on removing the redundancy among multiple phrases and organizing the final abstractive caption. Additionally, the order and quality of region of interest and modality processing are studied to give insights of better understanding the influence of regional visual cues on language forming. Experiments demonstrate the effectiveness of the proposed method.