Reviewerly/exHarmony huggingface dataset:
Runtime Security Policy Enforcement in Clouds

In this chapter, we explain the design and implementation of a middleware, namely PERMON, to apply the proactive approach to OpenStack (OpenStack open-source cloud computing software (2015). http://www.openstack.org . Accessed 14 Feb, 2018), which is one of the most popular cloud platforms. The middleware is designed to intercept the attributes of user-issued requests on their path to an intended API service, and identify the requested event types based on the examined attributes. Having processed the selected parameters coupled with the identified event types, the middleware enforces the verification result by either granting or rejecting the user request.
Interactive Learning-Based Retrieval Technique for Visual Lifelogging

Currently, there is a plethora of video wearable devices that can easily collect data from daily user life. This fact has promoted the development of lifelogging applications for security, healthcare, and leisure. However, the retrieval of not-pre-defined events is still a challenge due to the impossibility of having a potentially unlimited number of fully annotated databases covering all possible events. This work proposes an interactive and weakly supervised learning approach that is able of retrieving any kinds of events using general and weakly annotated databases. The proposed system has been evaluated with the database provided by the Lifelog Moment Retrieval (LMRT) challenge of ImageCLEF (Lifelog2018), where it reached the first position in the final ranking.
A Spatial-Aware Tracker

In this paper, a novel spatial-aware tracker (SAT), which utilizes the Siamese network and multiple correlation filters, is proposed to deal with fast motion and model drift problem in visual tracking. Specifically, the Siamese network is first used by an adaptive spatial search strategy to detect the target object in larger search areas. An extended search patch is generated if the target position obtained by the Siamese network is far away from the previous target position. Then, multiple correlation filters perform detection operations on both the extended search patch and the original search patch. With the proposed spatial selection scheme, SAT can accurately track the target object in challenging tracking scenes. By taking advantage of the Siamese network and multiple correlation filters, the proposed SAT tracker can effectively deal with fast motion and model drift problems to achieve better tracking performance. Extensive experimental results demonstrate that the proposed SAT tracker performs superiorly against several state-of-the-art trackers on OTB-2015 tracking benchmark.
Identification and Implementation of the Intellectual Potential of Students Through Intellectual Activities: Open and Accessible Developing Environment

The article provides examples of open and accessible intellectual events for students, including international, which contribute to the identifi cation and development of intellectual abilities in the study of certain general educational subjects, as well as form an interest in research activities, the desire for knowledge of scientifi c and subject areas
A Gamut-Extension Method Considering Color Information Restoration using Convolutional Neural Networks

Recently, Ultra HDTV (UHDTV) services become popular over satellite and on the internet. On the contrary, there are tremendously huge volume of High Definition Television (HDTV) and Standard Definition Television (SDTV) contents stored in broadcasting companies and storage devices. In this paper, we propose a color space conversion (also known as gamut mapping) method from BT. 709 (used for current HDTV broadcast) to BT. 2020 (used for UHDTV broadcast), which estimates and restores lost color information. It learns an end-to-end conversion method from BT. 709 image to BT. 2020 image with restoring lost color information using Convolutional Neural Network (CNN). By experiments, we confirm that our method can achieve 2.31dB gain against the conventional method on average.
A survey of time based approaches for linked data

Purpose Linked data technologies promise different ways of querying and retrieving information that enable individuals to have search experiences that are broader and more coordinated than those common in current library technologies. It is vital that information technologies be able to incorporate temporal capabilities or reasoning to allow for the more nuanced interactions with resources, particularly as they change over time. The purpose of this paper is to assess methods currently in use that allow for temporal querying of resources serialized as linked data. Design/methodology/approach This paper examines philosophical models, experimental approaches and common standards to identify areas of alignment and divergence in their orientations toward serializing time and change as linked data. By framing approaches and standards within the context of philosophical theories, a clear preference for certain models of time emerge. Findings While there have been several approaches to serializing time as linked data, none have found their way into a full implementation by standards in common use. Further, approaches to the issue are largely rooted in one model of philosophical thought that is particularly oriented to computational approaches. As such there is a gap between methods and standards, and a large room for further investigation into temporal models that may be applicable for different contexts. A call for investigation into a model that can cascade in to different temporal approaches is provided. Originality/value While there are many papers concerning serializing time as linked data, none have tried to thoroughly align these to philosophical theories of time and further to standards currently in use.
A framework for big-step semantics

The semantics of programming languages or software systems generally specifies, for each program/system configuration, its final result, if any. In literature, there are two main approaches to define such a relation in an operational way: either on top of a small-step relation [12, 13], representing single computation step, or directly by a set of rules as in big-step style [9].
Binocular Vision System Integrated with Transcranial Magnetic Stimulation Automatic Therapeutic Apparatus

For achieving precise positioning of Transcranial Magnetic Stimulation Automatic Therapeutic Apparatus (TMSATA) and conduct the kinematic calibration between camera coordinate system and nuclear magnetic resonance (NMR) coordinate system, a binocular vision system is employed to measure the position and orientation of target makers. TMSATA based on 6-UPS parallel mechanism is proposed to improve accuracy and rapidity. Stereo calibration and stereo correction are used to calibrate the binocular vision system. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm was adopted to detect the region of the target makers, and then the center is fitted with the least-squares method, and the results were compared with that from Hough-circle test. Finally, the coordinates of the target maker in the camera coordinate system are measured through experiments, which verifies the feasibility of the binocular vision system.
The use of Petri nets in the study of networked control systems

The use of communication networks in control systems has several important advantages, such as the ability of information transfer and remote control of various objects, the possibility of modifications and maintenance. On the other hand, the time between reading measurements from the sensor and sending a control signal to the actuator depends on the network characteristics (topology and routing scheme), and such a time delay can greatly affect the overall network performance. Delays, distortions and loss of transmitted data not only degrade the performance of the network management system, but also destabilize it. The paper considers the use of Petri nets as a method for modeling networked control systems (NCS) on the example of designing an active suspension control system for a car. When modeling, the star and common bus topologies were used, the comparison of which revealed that control systems with the common bus topology function 40% faster than systems with the star topology.
Deep reinforcement learning-based patch selection for illuminant estimation

Previous deep learning based approaches to illuminant estimation either resized the raw image to lower resolution or randomly cropped image patches for the deep learning model. However, such practices would inevitably lead to information loss or the selection of noisy patches that would affect estimation accuracy. In this paper, we regard patch selection in neural network based illuminant estimation as a controlling problem of selecting image patches that could help remove noisy patches and improve estimation accuracy. To achieve this, we construct a selection network (SeNet) to learn a patch selection policy. Based on data statistics and the learning progression state of the deep illuminant estimation network (DeNet), the SeNet decides which training patches should be input to the DeNet, which in turn gives feedback to the SeNet for it to update its selection policy. To achieve such interactive and intelligent learning, we utilize a reinforcement learning approach termed policy gradient to optimize the SeNet. We show that the proposed learning strategy can enhance the illuminant estimation accuracy, speed up the convergence and improve the stability of the training process of DeNet. We evaluate our method on two public datasets and demonstrate our method outperforms state-of-the-art approaches.
Path Planning of Sand Blasting Robot Based on Improved RRT Algorithm

In order to solve the problem that the sand blasting robot can complete the automatic/semi-automatic blasting in the blasting workshop according to the geometry of the blasting workpiece. This paper proposes a kind of adapt to the traversal of a variety of sandblasting workpieces and path planning methods for obstacle avoidance based on the fast extended random tree (RRT) algorithm. In particular, an improved method for trajectory planning for obstacle avoidance is proposed. Firstly, the sandblasting workpieces are divided into regions and the traversing trajectory planning of the sandblasting area according to the large-scale and diversity characteristics of sandblasting workpieces. Then, the improved RRT algorithm is used to plan the obstacle avoidance trajectory of the simulated traversal trajectory. Through the simulation verification of the method, it is possible to realize a comprehensive, safe, fast and accurate path planning for different blasting workpieces and with faster processing speed than traditional algorithms.
El Hareketleri ve Ses ile Sanal Ayna Kontrolü

Although online shopping has been very popular recently, customers have some disadvantages such as the impossibility to try the product before buying. Owing to the recent technological advances, people can try on products to be purchased on a virtual mirror. Inspired by this situation, it has been developed a system which users can try handbags on a monitor and see different poses of handbags as if they wear them. Also, users can see different colors of handbags by saying the color that they want to see to the system. In this application, it is taken advantage of Kinect Sensor to follow user motions and hand skeleton joints which is placed on the bag. The voice command recognition is used which is based on correlation method.
Feature Fusion from Multiple Paintings for Generalized Artistic Style Transfer

The paper presents a deep CNN based artistic style transfer approach which automatically superimposes the artistic style on any input photograph. The generated image has a similar visual appearance to that of the artist’s painting(s). Existing approaches to artistic style transfer use a single painting of an artist to generate his/her painting style. We propose a more general framework in which a generalized style feature is extracted by fusing style feature from multiple paintings of an artist. A pre-trained VGG model is employed which extracts the generalized style feature from the artist’s paintings and the content feature from the input photograph. In this work, we study the different techniques for style feature fusion and present a new technique to evaluate the potency of any style transfer technique. Experimental evaluation indicates that the proposed style feature fusion technique captures the artistic style at a higher resolution than existing approaches which uses only a single painting from each artist for style transfer.
Evaluation of Representation Fidelity to Similarity in ChronoView

ChronoView is a visualization method representing periodic features of event occurrence times. It represents each event group (i.e., each set of time stamps) as a position on a plane. While ChronoView has high space efficiency, it has representational ambiguity. For example, while two similar sets must be placed at positions close to each other, two sets placed at positions close to each other need not always be similar. A three-dimensional development of ChronoView has been proposed to solve this problem. This paper presents a numerical experiment to evaluate how similarities between event groups are faithfully represented by Euclidean distance in a presentation space. The experiment shows that the three-dimensional ChronoView's faithfulness is higher than that of two-dimensional ChronoView and lower than that of three-dimensional multidimensional scaling (MDS) but comparable to that of two-dimensional MDS.
Critique of 'let them fail

We supply a critique of the paper Let Them Fail: Towards VM built-in behavior that falls back to the program, suggesting directions in which its work of negotiating the boundary between the responsibilities of VMs and their hosted programs can be continued, and questioning our current disciplinary separation between systems and languages research.
Quantum Reversible Fuzzy Grammars

Reversible languages are a class of regular languages that stands at the junction of several domains. While quantum reversible regular grammars are a special class of fuzzy regular grammars, the algebraic characterizations of quantum reversible regular languages have not been studied so far. This paper establishes quantum reversible fuzzy grammars and fuzzy (hyper-) regular grammars, and presents that the set of quantum reversible regular languages coincides with the set of all the quantum reversible hyper-regular languages. Moreover, it is shown that the set of quantum reversible regular languages coincides with that of quantum languages accepted by quantum reversible fuzzy automata. Particularly the algebraic properties of quantum reversible fuzzy grammars are discussed.
Reflections on computing beyond pencils

Marasoiu and Church [9] (hereafter M&C) highlight two critiques of current software. First, poverty of tools and media:
Visualizing a Linguistic Ontology with Ling-Graph

We are proposing a semantic approach that aims to identify valid linguistic web services composition scenarios. It targets both linguistic and software engineering experts. It is based on an OWL 2 multilingual ontology, named LingOnto which models and reasons about linguistic knowledge. However, users especially non-ontology experts have difficulty to make sense of LingOnto as they do not understand OWL 2 syntax. Hence, we decide to visualize LingOnto to attempt this issue. Nevertheless, the current ontology visualization tools overlook the importance of the understandability issues which are particularly problematic for non-ontology experts. In this paper, we propose a user-friendly ontology visualization tool, called Ling-Graph. It targets both ontology and non-ontology experts and addresses the understandability requirement. This tool is also applied to visualize the PersonLink ontology for non-ontology experts and a large-scale ontology DBpedia for ontology experts. Finally, we discuss the promising results derived from the evaluation of Ling-Graph.
The Factors Which Influence the Level of Usage Success of HCIS (Human Capital Information System) (Case Study of BPJS Ketenagakerjaan)

HCIS (Human Capital Information System) is human resources management system at Badan Penyelenggara Jaminan Sosial (BPJS) Ketenagakerjaan (Labour Social Security Administration Agency). The purpose of implementing HCIS is to provide the best service for BPJS Ketenagakerjaan employees while the study of it was to analyze the success in using it with variables test such as system and information quality, perceived easy of use, perceived usefullness and user satisfaction by using the Structural Equation Model (SEM) analysis model. The samples were taken from 449 respondents and analytical tools used are The Measurement Validity Test (Absolute Fit Test, Fit Incremental Test) and Analysis the Indicators and Variables with AMOS (Analysis Moment of Structure) software. Based on the data analysis, the results show that all variables test has a positive effect on user satisfaction, and user satisfaction has a positive influence on usage success.
Smart: A Sensor-Triggerred Interactive Mr Display

To increase the user experience in mixed reality (MR) applications, researchers proposed to display virtual objects in mirrors and transmissive mirror devices. For example, [1] proposed to detect the moving object hold by a user in the real world and display the smoke virtual visual effect in a mirror with an MR experience. On the other hand, [2] developed the MRsionCase to display the text information near the static sculpture. In recently years, researchers paid attention to display the virtual contents in head-mount-display (HMD) devices. To name a few, [3] proposed to use a VR keyboard model with MR real hands to enhance an immersive experience. Furthermore, [4] proposed to use an Ultrahaptics to generate a sensing experience on a finger of a user in an MR application. However, to represent consistent displaying effect from a virtual space to a real space (the physical world) is still a challenging research issue. In this paper, a prototype with a hologram display is developed as an interactive MR device, triggered by the sensor values on mobile devices to change the attitude of a virtual object in the virtual world and the real object in a physical world with a synchronizing manner.
Multimodal Latent Factor Model with Language Constraint for Predicate Detection

Nowadays, visual relationship detection has shown an important utility in scene understanding. Predicate detection, which aims to detect the predicate between entities in an image, is an important part of visual relationship detection. In this paper, we propose Multimodal Latent Factor Model with Language Constraint (MMLFM-LC) for predicate detection with the novelty of integrating knowledge learned from multiple modalities, valid relationships and semantical similarities. Representations of visual and textual modalities are firstly input into the constructed model. Secondly, a bilinear structure is introduced to model the relationships using valid relationships, while a language constraint is also built utilizing semantical similarities. Lastly, visual and textual representations are fused in an embedded subspace for predicate detection. Experiments on both Visual Relationship and Visual Genome datasets show that our method outperforms other methods on predicate detection.
Facilitating the Support of Cloud-Based Service Marketplaces

Online marketplaces allowing the automatic orchestration of cloud-based services present itself as one face to the customer - a place where customers can book, manage, and combine services into individual systems. Platform providers thus need to offer first level support in case of errors. Support staff, however, may have no access to details from services provided by third parties, making the identification of defect causes challenging. We propose to combine techniques from agenda-driven case management and anomaly detection to help support staff locate error sources in interconnected third-party services.
TECHNOLOGY OF DESIGNING DIGITAL DIDACTIC VISUAL MEANS IN PROFESSIONAL ACTIVITIES OF TEACHER

The article defines the essence and features of the design of digital didactic visual means in the professional activity of the teacher. The fact that there are no pedagogical researches devoted to the specifics of the creation of didactic visual digital means is revealed at the present time, however, the creation of qualitative visual materials requires appropriate competencies, which necessitates disclosure of the specifics of their creation and the corresponding technology. The peculiarity of the design of the digital didactic visual means, which is due to the compositional decisions, typography, coloristics, includes the unity of style and the creation of visual anchors and metaphors. The technology of designing the indicated means consisting of several stages is substantiated, namely: the stage of goal-setting, the stage of didactic design, the stage of visual design, the stage of instrumental design. The stage of goal-setting involves determining the significance and location of the training material in the general structure of the discipline in terms of the relevant topic section; finding out the specifics of the contingent of students; analysis of the complexity of creating a suitable visual tool. The stage of didactic design involves analytical work with information; identification of key elements of informational content; establishing interrelations and hierarchy. The stage of visual design aims to transfer the layout in a visual form and involves taking into account composite decisions, rules of coloring and typography and the adding of visual anchors and metaphors. The final step is to transfer the prototype to a digital format and involves the program realization and receipt of the final product of the digital visual didactic tool. The proposed technology systematizes the process of developing a didactic visual tool and consists of logical interconnected sequential stages which have a certain result. The structure, content and methods of teacher’s design activity at each of the stages are revealed. The proposed technology can be used in the process of training future educators and in the system of postgraduate education.
Object Bounding Transformed Network for End-to-End Semantic Segmentation

In recent years, numerous studies of the use of a Fully Convolutional Network (FCN) for image semantic segmentation have been published. This work introduces an end-to-end Object Bounding Transformed Network (OBTNet) which combines the advantages of the Object Boundary Guided (OBG) and Doman Transform (DT). OBG is an object boundary based approach that increases the integrity of object shape. Based on OBG, we propose an Object Boundary Network (OBN) as the object region and object boundary generator. In addition, our system achieves object region preserving and object boundary preserving by employing DT. The proposed system uses the pretrained multi-scale ResNet101 as the base network and uses multi-scale atrous convolution to preserve the dimensions of the feature map, increasing the accuracy of semantic segmentation. Experiments show that our system yielded a mean IOU of 77.74% and outperformed the baseline model on the VOC2012 test set.