Reviewerly/exHarmony huggingface dataset:
Color Image Quality Assessment Based on VIF

This work aims to improve the performance of S-VIF by incorporating chrominance channels into the algorithm. The perceptual color difference is measured by a superpixel-based CIEDE2000 extension. Color difference and visual saliency are used as pooling weights to determine the different importance of each local quality estimation of an image.
Multi-Task Learning for Authorship Attribution via Topic Approximation and Competitive Attention

Separating content from style is a fundamental problem in authorship attribution to represent topic independent personal style of authors. Previous work often ignores this problem by imposing strong but unrealistic assumptions or artificially determines a set of predefined stylistic structures. This paper proposes to separate topic and style based on neural multi-task learning. Our target is to learn separate representations for topic and style respectively. In addition to authorship attribution as the main task, we introduce a novel auxiliary task topic approximation to guide the learning of topic representations with the topic distributions inferred by topic models, which are trained from external corpus. Moreover, we propose a competitive attention mechanism and a separation-reconstruction constraint to assign different and competitive attentions to two tasks in order to separate topic and style as much as possible. Evaluation results demonstrate that the proposed multi-task learning based method is promising, especially on cross-topic settings. We found that topic approximation can help capture topical content and the competitive attentions benefit topic-style separation. It is encouraging since our model separates topic and style in a probabilistic way and doesn't require human intervention.
Modeling Moods

CONTEXT: Visual modeling languages like UML and BPMN are often used to represent and communicate knowledge about a software systems and business processes. In this function, modeling languages compete with prose texts expressed in natural languages. This begs the question, which of the two is "better". We argue that one particularly important feature of natural languages is the epistemic mood, i.e., the modality expressing degrees of reality: natural languages allow to express "is" as well as "could be" and "will be". Modeling languages like the UML offer no such flexibility. AIM: We strive to improve modeling utility and practice by introducing modalities in modeling languages. We show how this can be done with little effort for existing modeling languages and tools, and demonstrate how this novel facility can be exploited for practical purposes. METHOD: We define the notion model modality and explore practical modeling examples to arrive at a set of common (useful) modalities. We design visualizations for modalities to maximize understandability and minimize effort. We study how they can be integrated into existing modeling languages like UML or BPMN. RESULTS: We find several obvious model modalities with straightforward practical applications. They afford natural visualizations and can be applied in existing tools. CONCLUSIONS: Contemporary modeling can be enriched by modality annotations resulting in increased expressiveness of models at minimal extra effort. Our approach integrates with existing methods, notations, and tools.
Which LSTM Type is Better for Interaction Force Estimation?

Tactile, one of the five senses classified into the main senses of human, is the first sensation developed when human beings are formed. The tactile includes various information such as pressure, temperature, and texture of objects, it also helps the person to interact with the surrounding environment. One of the tactile information, the pressure is used in various fields such as medical, beauty, mobile devices and so on. However, humans can perceive the real world with multi-modal senses such as sound, vision. In this paper, we study interaction force estimation using haptic sensor and video. Interact ion force estimation through video analysis is one of a cross-modal approach that is applicable such as a software haptic feedback method that can give haptic feedback to remote control of robot arm by predicting interaction force even in absence of haptic sensor. we compare and analyze three types of a deep neural network to predict the interaction force. In particular, the best model for the stacking structure of CNN and LSTM is selected through a detailed analysis of how the structure change of LSTM affects the video regression problem. The average error of the best suit model is MSE 0.1306, RMSE 0.2740, MAE 0.1878.
Analyzing Time Attributes in Temporal Event Sequences

Event data is present in a variety of domains such as electronic health records, daily living activities and web clickstream records. Current visualization methods to explore event data focus on discovering sequential patterns but present limitations when studying time attributes in event sequences. Time attributes are especially important when studying waiting times or lengths of visit in patient flow analysis. We propose a visual analytics methodology that allows the identification of trends and outliers in respect of duration and time of occurrence in event sequences. The proposed method presents event data using a single Sequential and Time Patterns overview. User-driven alignment by multiple events, sorting by sequence similarity and a novel visual encoding of events allows the comparison of time trends across and within sequences. The proposed visualization allows the derivation of findings that otherwise could not be obtained using traditional visualizations. The proposed methodology has been applied to a real-world dataset provided by Sheffield Teaching Hospitals NHS Foundation Trust, for which four classes of conclusions were derived.
Report on the First HIPstIR Workshop on the Future of Information Retrieval

The vision of HIPstIR is that early stage information retrieval (IR) researchers get together to develop a future for non-mainstream ideas and research agendas in IR. The first iteration of this vision materialized in the form of a three day workshop in Portsmouth, New Hampshire attended by 24 researchers across academia and industry. Attendees pre-submitted one or more topics that they want to pitch at the meeting. Then over the three days during the workshop, we self-organized into groups and worked on six specific proposals of common interest. In this report, we present an overview of the workshop and brief summaries of the six proposals that resulted from the workshop.
Location Privacy Using Data Obfuscation in Fog Computing

In the past few decades, smartphones and Global Positioning System(GPS) devices have led to the popularity of Location Based Services. It is crucial for large MNCs to get a lot of data from people and provide their services accordingly. However, on the other side, the concern of privacy has also increased among the users, and they would like to hide their whereabouts. The rise of data consumption and the hunger for faster network speed has also led to the emergence of new concepts such as the Fog Computing. Fog computing paradigm extends the storage, networking, and computing facilities of the cloud computing towards the edge of the networks while removing the load on the server centers and decreasing the latency at the edge device. The fog computing will help in unlimited growth of location services and this adoption of fog computing calls for the need for more secure and robust algorithms for location privacy. One of the ways we can alter the information regarding the location of the user is Location Obfuscation. This can be done reversibly or irreversibly. In this paper, we address the problem of location privacy and present a solution based on the type of data that has to be preserved (in our case, it is distance). A mobile application has been designed and developed to test and validate the feasibility of the proposed obfuscation techniques for the Fog computing environments.
Extended Hoeffding Adaptive Tree based-Server Load Prediction in Cloud Computing environment

Cloud Computing (CC) enables client-server relationship in order to release users from computational and storage responsibility. As multi-tenant environment, Cloud providers are dealing, in one hand, with multiple concurrent users each of which exhibits a different and variable behavior over time and in the other hand, with a performance interference due to the co-location of multiple virtual machines (VMs) in the same server. Therefore, a real time server load prediction is needed in order to ensure efficient resource provisioning. While classical data mining based techniques suffer from important evaluation time and are enable to react to changes as it arrives, stream mining techniques can provide a real time prediction and changes detection. Thus, in this paper we used a well known stream mining technique, Hoeffding Adaptive Tree (HAT), in order to provide real time server load prediction. The aim of our proposed technique is to detect and react on the fly to different kind of changes that can affect the server load. Therefore, we augmented HAT by ensemble drift detectors in order to produce more accurate prediction. In order to evaluate our proposed technique HAT-ADS, we first compared it with a well known load prediction technique based on Bayesian approach. Then we compared our solution with another HAT based techniques. Overall, The experimentation showed that HAT-ADS proved important flexibility to various types of changes providing high accuracy with quick evaluation time and small memory footprint.
Disguised Face Detection

Research in face detection has seen tremendous growth over the past couple of decades. Beginning from algorithms capable of performing detection in constrained environments, the current face detection systems achieve very high accuracies on large-scale unconstrained face datasets. While upcoming algorithms continue to achieve improved performance, a majority of the face detection systems are susceptible to failure under disguise variations, one of the most challenging covariate of face detection. In this article, we propose a method for disguised face detection. The method consists of three stages. The first stage detects a face. The second one finds parts of the face on an output from the first stage. The third stage determines whether the face is disguised. Experiments show that our method can detect disguised faces in real time under the complex background and achieve acceptable disguised face detection rate.
Fast and slow dynamics for classical and quantum walks on mean-field small world networks

Abstract This work investigates the dynamical properties of classical and quantum random walks on mean-field small-world (MFSW) networks in the continuous time version. The adopted formalism profits from the large number of exact mathematical properties of their adjacency and Laplacian matrices. Exact expressions for both transition probabilities in terms of Bessel functions are derived. Results are compared to numerical results obtained by working directly the Hamiltonian of the model. For the classical evolution, any infinitesimal amount of disorder causes an exponential decay to the asymptotic equilibrium state, in contrast to the polynomial behavior for the homogeneous case. The typical quantum oscillatory evolution has been characterized by local maxima. It indicates polynomial decay to equilibrium for any degree of disorder. The main finding of the work is the identification of a faster classical spreading as compared to the quantum counterpart. It stays in opposition to the well known diffusive and ballistic for, respectively, the classical and quantum spreading in the linear chain.
Sensyml: Simulation Environment for large-scale IoT Applications

IoT systems are becoming an increasingly important component of the civil and industrial infrastructure. With the growth of these IoT ecosystems, their complexity is also growing exponentially. In this paper we explore the problem of testing and evaluating large scale IoT systems at design time. To this end we employ simulated sensors with the physical and geographical characteristics of real sensors. Moreover, we propose Sensyml, a simulation environment that is capable of generating big data from cyber-physical models and real-world data. To the best of our knowledge it is the first approach to use a hybrid integration of real and simulated sensor data, that is also capable of being integrated into existing IoT systems. Sensyml is a cloud based Infrastructure-as-a-Service (IaaS) system that enables users to test both functionality and scalability of their IoT applications.
Integrating CPS into Socio-Technical Systems

While nowadays machines, robots and other technical entities become more intelligent and the need of flexible as well as more efficient manufacturing and logistics systems is growing, the human worker is not yet an integrated part of these new emerging systems. Besides exposing the human to a vast amount of data, new collaborative work forms within these mainly decentralized systems are still missing. This article introduces a hierarchy of design levels for socio-technical systems, which emphasizes on the adaptivity of intelligent technical entities represented in a multi-agent system. Furthermore, the focus lies on possible natural depictions of information.
Mutli-Radio Rapid Handover Technology in Underground Mine Wireless Communication Networks

In underground mine, wireless networks usually use multi-hop chain topology. When a mobile station roams in it, the station switches from one access point to next access point to maintain the data communication. Generally, the communication delay and packet loss affect the movement operational equipment's real-time monitoring and controlling. In this paper, we proposed a multi-radio based rapid handover technology (MRRH) in underground mine wireless communication networks, it reduces the handover latency by optimizing the channel scanning process and redundant radio accessing. Test results show that MRRH can meet the underground mines' quality of service (QoS) requirement by reducing the channel scanning time and handover latency.
On the code modernization of shared sampling alpha matting with OpenMP

Image matting separates the foreground object from a given image by estimating the alpha values. It has been one crucial step in computer vision, as well as an essential technique in visual effects in the modern filmmaking industry. Due to its time complexity being proportional to the size of the unknown region, the computational speed for processing image matting on high-resolution images has been traditionally slow. In this paper, we present the code modernization of Shared Sampling Alpha Matting (SSAM) algorithm with OpenMP to speed up the computation on multicore server platforms. Significant efforts are required due to the sequential nature of the code, with data dependencies and complex data structures in the original code. The implementation of migrating the SSAM code to multicore platforms and the experiments performed on several server platforms of different categories/specs are presented. Our experimental results demonstrate that the Alpha Matting using OpenMP increases performance significantly on Intel Xeon Phi Knights Landing (KNL) processors when processing high-resolution images. The code improvements we achieved can support a parallel application developer with high-performance goals.
Media objects in architecture and art-design: materials and methods of creation

Abstract In this article the authors discuss the ways in which the use of media objects in architecture and art-design allows to easily adjust the space scenario depending on fast changing needs. The researchers consider the materials exploited for creating the media objects as well as present the classification of the media technologies in comparison to their interaction with people.
An Introduction to the Planning Domain Definition Language (PDDL): Book review

The article studies the ability of agents with bounded memory to execute consecutive composition of plans. It gives an upper limit on the amount of memory required to execute the composed plans and shows that the limit cannot be improved. Furthermore, the article shows that there are, essentially, no other universal properties of plans for bounded-recall agents expressible through the relation “there is a plan for an agent with a given memory size to navigate from one given set of states to another”.
Temporal determinization of mutating finite automata: Reconstructing or restructuring

Summary A mutating finite automaton (MFA) is a nondeterministic finite automaton (NFA) that changes its morphology over discrete time by a sequence of mutations. This results in a sequence of NFAs, the initial NFA, and one mutated NFA for each mutation. Some application domains, including model‐based diagnosis of discrete‐event systems in artificial intelligence and model‐based testing in software engineering, require temporal determinization of MFAs. Determinizing an MFA temporally means generating a deterministic finite automaton (DFA) that is equivalent to the mutated NFA as soon as a mutation occurs. Since, in computation time, the classical Subset Construction determinization algorithm may be less than optimal when applied to MFAs, a conservative algorithm is proposed, called Subset Restructuring , which, instead of constructing the new DFA from scratch based on the mutated NFA, generates the new DFA by updating the previous DFA based on the mutation occurred. Subset Restructuring is sound and complete, thereby yielding the same DFA generated by Subset Construction . Results from massive experimentation indicate the viability of Subset Restructuring , especially so when large MFAs change by small mutations.
A Novel Approach to Video Steganography using a 3D Chaotic Map

In this paper, we introduce a novel approach for data-hiding in videos using 3-dimensional Chaotic Maps. A video is represented as a 3-dimensional image, with the third axis constituting the frames of the video. Existing chaotic map based data-hiding techniques on videos is confined to applications of 2-dimensional chaotic maps on a per-frame basis. In this paper, a 3-dimensional extension of the logistic chaos map is applied to identify pixels to encode information in the video's 3-dimensional space and 3-3-2 Least Significant Bit (LSB) substitution is used to encode 1 byte of information per pixel. We have implemented and presented a proof of concept that has been analyzed on a test video using various quality metrics. The chaotic map based data-hiding approach proposed in this paper is shown to be secured and the results observed are inline with the standard results for a video steganographic algorithm using LSB substitution.
Minimal determination of a pure qutrit state and four-measurement protocol for pure qudit state

Finding the least measurement settings to determine an arbitrary pure state has been long known as the Pauli problem. Peres conjectured that two unbiased bases may be sufficient to determine a pure state up to some finite ambiguities. Here we find that the Peres conjecture is correct in the case d = 3 where a pure state is determined up to at most six candidates states and is incorrect in the case of d = 4 for which a counterexample is constructed. We observe that the target state can be picked out from candidates states by an adaptive two-outcome measurement. We thus provided a minimal qutrit tomography protocol constituted of three measurements in contrast to the four measurements required in previous non-adaptive method. We also reduce a five-measurement protocol for a pure qudit state into three full-dimensional measurements and one two-outcome measurement protocol, which exhibits robustness to the usual white noise thus also applies to nearly pure state.
No Accounting for Taste: Supporting Developers' Individual Choices of Coding Styles

When creating their programs, developers usually have a preferred or standardized style of their own to write code, known as coding style. Such code is usually stored in a version control repository, through which collaborative work usually takes place. However, in such a setting, isolated attempts of standardization can lead to several coding styles coexisting in the same project, causing the opposite effect to that intended. Besides increasing the effort required to understand code, coding style conflicts may also clutter repository history as developers change existing styles to their usual preferences. To overcome this problem, we propose an approach to support the definition of a repository coding style while allowing developers to use their preferred coding style. To illustrate our approach, we built the RECoSt tool and applied it using real excerpts of a popular open source project. Our proposed approach intends to help developers keep their projects' coding style standardized without having to abandon the style they are familiar with.
Balancing Consistency and Plot Structure in Computational Storytelling

With technological advancements, much effort has been put to develop a computational story generator. One of the prime challenges for computational story generators is balancing the consistency and structure of the plot. Although a few attempts have been made to address this challenge, there is still a lot to desire. In this paper, we propose a computational story generator, based on Makebelieve, that while allowing authors to define plot points in the story, offers consistency with a controllable amount of tolerance. We use Directed Causal Graph and dependency matrix along with planning to generate the events. We then check the fitness of the plan by considering the consistency of character personality throughout the plan. With the example stories, we discuss how the proposed approach affects the plot to ensure consistency along with plot structure.
Multiobjective noncooperative game model for cost‐based task scheduling in cloud computing

Summary Task scheduling is an important issue in Cloud Computing. Cloud Computing often covers a range of different kinds of nodes, which offers a complex environment. To handle this complexity, as while as to process task scheduling in different scenarios of Cloud Computing, many algorithms have been studied. In this paper, we propose a game theory–based algorithm to the Cloud Computing task scheduling problem on the principle of minimizing the cost in the Cloud Computing. The Cloud Computing task scheduling problem is treated as a noncooperative game. The experiment results demonstrate that the game‐based algorithm has a desirable capability to reduce the cost in the Cloud Computing.