Reviewerly/exHarmony huggingface dataset:
Cerberus: Applying Supervised and Reinforcement Learning Techniques to Capture the Flag Games

Applying machine learning techniques to real-time computer games is an active research field. In this paper we present Cerberus, a machine learning framework for team-based Capture The Flag (CTF) games. This framework utilizes reinforcement learning to select high-level actions that achieve best team behaviour and utilizes neural networks to control fighting behaviour of team individuals. Our proposed framework also combines waypoints and influence maps for effective path planning
To UCT, or not to UCT? (Position Paper)

Monte-Carlo search is successfully used in simulation-based planning for various large-scale sequential decision problems, and the UCT algorithm seems to be the choice in most (if not all) such recent success stories. Based on some recent discoveries in theory and empirical analysis of Monte-Carlo search, here we argue that, if online sequential decision making is your problem, and Monte-Carlo tree search is your way to go, then UCT is unlikely to be the best fit for your needs.
Finding and Exploiting LTL Trajectory Constraints in Heuristic Search

We suggest the use of linear temporal logic (LTL) for expressing declarative information about optimal solutions of search problems. We describe a general framework that associates LTLf formulas with search nodes in a heuristic search algorithm. Compared to previous approaches that integrate specific kinds of path information like landmarks into heuristic search, the approach is general, easy to prove correct and easy to integrate with other kinds of path information.
Characterisation of the Degree of Musical Non-Markovianity

As an aid for musical analysis, in computational musicology mathematical andinformatics tools have been developed to characterise quantitatively some aspectsof musical compositions. A musical composition can be attributed by ear a certainamount of memory. These results are associated with repetitions and similarities ofthe patterns in musical scores. To higher variations, a lower amount of memory isperceived. However, the musical memory of a score has never been quantitativelydefined. Here we aim to give such a measure following an approach similar tothat used in physics to quantify the memory (non-Markovianity) of open quantumsystems. We apply this measure to some existing musical compositions, showingthat the results obtained via this quantifier agree with what one expects by ear.The musical non-Markovianity quantifier can thus be used as a new tool that canaid quantitative musical analysis. It can also lead to future quantum computingcontrollers to manipulate structures in the framework of generative music.
ASL-STEM Forum: A Bottom-Up Approach to Enabling American Sign Language to Grow in STEM Fields

Deaf and hard of hearing students studying advanced topics in Science, Technology, Engineering, and Mathematics (STEM) lack standard terminology to enable them to learn, discuss and contribute to their chosen fields. The ASL-STEM Forum enables the diverse, thinly-spread groups that are independently creating and us1ing terminology to come together using a community-based, video-enabled web resource. A common vocabulary would provide interpreters with consistent terminology and enable deaf scientists to more easily converse from a common basis. This paper discusses the implementation of the ASL-STEM Forum, describes our approach to building a community using the site, and overviews the unique opportunities it offers for observing a language developing from the bottom-up.
Harmonic Navigator: A Gesture-Driven, Corpus-Based Approach to Music Analysis, Composition, and Performance

We present a novel, real-time system for exploring harmonic spaces of musical styles, to generate music in collaboration with human performers utilizing gesture devices (such as the Kinect) together with MIDI and OSC instruments / controllers. This corpus-based environment incorporates statistical and evolutionary components for exploring potential flows through harmonic spaces, utilizing power-law (Zipf-based) metrics for fitness evaluation. It supports visual exploration and navigation of harmonic transition probabilities through interactive gesture control. These probabilities are computed from musical corpora (in MIDI format). Herein we utilize the Classical Music Archives 14,000+ MIDI corpus, among others. The user interface supports real-time exploration of the balance between predictability and surprise for musical composition and performance, and may be used in a variety of musical contexts and applications.
Efficient Recommendation of De-Identification Policies Using MapReduce

Many data owners are required to release the data in a variety of real world application, since it is of vital importance to discovery valuable information stay behind the data. However, existing re-identification attacks on the AOL and ADULTS datasets have shown that publish such data directly may cause tremendous threads to the individual privacy. Thus, it is urgent to resolve all kinds of re-identification risks by recommending effective de-identification policies to guarantee both privacy and utility of the data. De-identification policies is one of the models that can be used to achieve such requirements, however, the number of de-identification policies is exponentially large due to the broad domain of quasi-identifier attributes. To better control the trade off between data utility and data privacy, skyline computation can be used to select such policies, but it is yet challenging for efficient skyline processing over large number of policies. In this paper, we propose one parallel algorithm called SKY-FILTER-MR, which is based on MapReduce to overcome this challenge by computing skylines over large scale de-identification policies that is represented by bit-strings. To further improve the performance, a novel approximate skyline computation scheme was proposed to prune unqualified policies using the approximately domination relationship. With approximate skyline, the power of filtering in the policy space generation stage was greatly strengthened to effectively decrease the cost of skyline computation over alternative policies. Extensive experiments over both real life and synthetic datasets demonstrate that our proposed SKY-FILTER-MR algorithm substantially outperforms the baseline approach by up to four times faster in the optimal case, which indicates good scalability over large policy sets.
Doris: An Adaptive Soft Real-Time Scheduler in Virtualized Environments

With the development of cloud computing and virtualization technologies, more and more soft real-time applications, such as Voice over Internet Protocol (VoIP) server and cloud gaming, are running in virtualized data centers. Though previous studies optimize CPU schedulers of hypervisors to support these applications in virtualized environments, there are some important challenges in designing an efficient CPU scheduler which is suitable for real-world clouds. On one hand, hypervisors do not know whether an application in a virtual machine (VM) has real-time requirements, so manually setting the scheduling parameters is a common case for CPU schedulers, which probably increases users' burden, lacks flexibility, and causes misconfigurations. On the other hand, it has been reported that most of existing CPU schedulers designed for soft real-time applications have an obvious propensity to such applications which prevents them from being applied in practical multi-tenant cloud environments. In this paper, we design and implement an adaptive soft real-time scheduler based on Xen, named Doris, to address these challenges. It identifies the VMs running soft real-time applications (RT-VMs) and infers their scheduling parameters according to the communication behaviors of VMs adaptively. Then, it promotes the priorities of VCPUs of the RT-VMs temporarily according to I/O events and the inferred scheduling parameters of RT-VMs to support soft real-time applications adaptively while minimizing the impacts on non-real-time applications. Finally, considering the importance of privileged entities (such as Domain0 in Xen) in I/O processing, Doris sets their types and scheduling parameters dynamically, which enables the adaptive scheduling of them to guarantee the performance of soft real-time applications. Our evaluation shows Doris can support soft real-time applications adaptively and efficiently, and only introduces very slight overhead.
Content Aware Image Pre-Compensation

The goal of image pre-compensation is to process an image such that after being convolved with a known kernel, will appear close to the sharp reference image. In a practical setting, the pre-compensated image has significantly higher dynamic range than the latent image. As a result, some form of tone mapping is needed. In this paper, we show how global tone mapping functions affect contrast and ringing in image pre-compensation. We further enhance contrast and reduce ringing by considering the visual saliency. Specifically, we prioritize contrast preservation in salient regions while tolerating more blurriness elsewhere. For quantitative analysis, we design new metrics to measure the contrast of an image with ringing. Specifically, we set out to find its “equivalent ringing-free” image that matches its intensity histogram and uses its contrast as the measure. We illustrate our approach on projector defocus compensation and visual acuity enhancement. Compared with the state-of-the-art, our approach significantly improves the contrast. We also perform user studies to demonstrate that our method can effectively improve the viewing experience for users with impaired vision.
Interaction Detection with Bayesian Decision Tree Ensembles

Methods based on Bayesian decision tree ensembles have proven valuable in constructing high-quality predictions, and are particularly attractive in certain settings because they encourage low-order interaction effects. Despite adapting to the presence of low-order interactions for prediction purpose, we show that Bayesian decision tree ensembles are generally anti-conservative for the purpose of conducting interaction detection. We address this problem by introducing Dirichlet process forests (DP-Forests), which leverage the presence of low-order interactions by clustering the trees so that trees within the same cluster focus on detecting a specific interaction. We show on both simulated and benchmark data that DP-Forests perform well relative to existing interaction detection techniques for detecting low-order interactions, attaining very low false-positive and false-negative rates while maintaining the same performance for prediction using a comparable computational budget.
Modeling self-organizing teams in a research environment

This paper presents the results of studies aimed at analyzing the effectiveness of a research center.The study focuses on the process of self-organization of project teams (groups of co-authors) for project implementation (writing a scientific article).The initiative to create a team comes from one of its members.The paper describes a formal model, based on a competence approach, which considers the types of tasks to be solved and the necessary skills of the staff.The paper also presents the results of simulation in the AnyLogic environment and problems for further research.The competency profile of each employee is a vector where each coordinate describes the level of mastery of the corresponding skill.The competency profile of the team is a vector obtained as the result of simple addition of the competency profiles of the participants.The proposed model assumes that each task requires a certain set BUSINESS INFORMATICS Vol. 13 No 2 -2019of competencies and that the list of competencies and the level of experience are the criteria for deciding whether to join the team.The logic of decision making at various stages of team creation is modelled by functions.At each step of the modelling, the next employee is chosen randomly.To calibrate the team member's competency profile, internal data on employee qualifications of the Gazpromneft Research Center was used.The constructed model is the basis for further studies of the process by which project teams are created and function in a scientific environment and for developing a methodology to assess the effectiveness of the work of research teams.It helps to predict the need for personnel with different competencies, plan activities to improve the skills of employees and strengthen communication in the team.
Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation Capability

We propose shifted inner-product similarity (SIPS), which is a novel yet very simple extension of the ordinary inner-product similarity (IPS) for neural-network based graph embedding (GE). In contrast to IPS, that is limited to approximating positive-definite (PD) similarities, SIPS goes beyond the limitation by introducing bias terms in IPS; we theoretically prove that SIPS is capable of approximating not only PD but also conditionally PD (CPD) similarities with many examples such as cosine similarity, negative Poincare distance and negative Wasserstein distance. Since SIPS with sufficiently large neural networks learns a variety of similarities, SIPS alleviates the need for configuring the similarity function of GE. Approximation error rate is also evaluated, and experiments on two real-world datasets demonstrate that graph embedding using SIPS indeed outperforms existing methods.
Robustness Can Be Cheap: A Highly Efficient Approach to Discover Outliers under High Outlier Ratios

Efficient detection of outliers from massive data with a high outlier ratio is challenging but not explicitly discussed yet. In such a case, existing methods either suffer from poor robustness or require expensive computations. This paper proposes a Low-rank based Efficient Outlier Detection (LEOD) framework to achieve favorable robustness against high outlier ratios with much cheaper computations. Specifically, it is worth highlighting the following aspects of LEOD: (1) Our framework exploits the low-rank structure embedded in the similarity matrix and considers inliers/outliers equally based on this low-rank structure, which facilitates us to encourage satisfying robustness with low computational cost later; (2) A novel re-weighting algorithm is derived as a new general solution to the constrained eigenvalue problem, which is a major bottleneck for the optimization process. Instead of the high space and time complexity (O((2n)2)/O((2n)3)) required by the classic solution, our algorithm enjoys O(n) space complexity and a faster optimization speed in the experiments; (3) A new alternative formulation is proposed for further acceleration of the solution process, where a cheap closed-form solution can be obtained. Experiments show that LEOD achieves strong robustness under an outlier ratio from 20% to 60%, while it is at most 100 times more memory efficient and 1000 times faster than its previous counterpart that attains comparable performance. The codes of LEOD are publicly available at https://github.com/demonzyj56/LEOD.
Learning a Visual Tracker from a Single Movie without Annotation

The recent success of deep network in visual trackers learning largely relies on human labeled data, which are however expensive to annotate. Recently, some unsupervised methods have been proposed to explore the learning of visual trackers without labeled data, while their performance lags far behind the supervised methods. We identify the main bottleneck of these methods as inconsistent objectives between off-line training and online tracking stages. To address this problem, we propose a novel unsupervised learning pipeline which is based on the discriminative correlation filter network. Our method iteratively updates the tracker by alternating between target localization and network optimization. In particular, we propose to learn the network from a single movie, which could be easily obtained other than collecting thousands of video clips or millions of images. Extensive experiments demonstrate that our approach is insensitive to the employed movies, and the trained visual tracker achieves leading performance among existing unsupervised learning approaches. Even compared with the same network trained with human labeled bounding boxes, our tracker achieves similar results on many tracking benchmarks. Code is available at: https://github.com/ZjjConan/UL-Tracker-AAAI2019.
High Speed Soft Decision Decoding of Linear Codes Based on Hash and Syndrome Decoding

Digital transmission systems carry information from the source to the receiver using a physical medium such as cable, fiber optic or even propagation on a radio channel which isn't entirely reliable and causes the change of data originally emitted.Today the use of error correcting codes for protection and correction becomes an integral part in the design of communication systems and computers.In this work, we present a new interesting way to accelerate the decoding process of linear codes.The proposed method called Soft Decision Decoder by Hash Techniques (SDHT) is based on syndrome-decoding algorithm and hash techniques.The use of this latest allows reducing considerably the search time of all possible error patterns of weights less than a fixed threshold.SDHT is applicable on many linear codes and exploit the polynomial form to reduce again the run time decoding for cyclic codes.SDHT is successfully applied to decode some Bose Ray-Chaudhuri and Hocquenghem (BCH), Quadratic Residue (QR) and Extended Quadratic Residue (EQR) codes.The simulation results show that the proposed SDHT yield to good error correcting performances with reduced complexity.The comparison between SDHT and many competitors shows that it gives better performances in terms of correction rate.The experimental study of the decoding steps for the BCH(63,45,7) code shows that the time search of the most likely error pattern is reduced at about 26214153% comparing to an exhaustive search of all possible error patterns of weights less than or equal to 4. This study proves the huge success of the proposed SDHT decoder.
A Real-World SPA

RealWorld ( ) is a project that demonstrates how to build a full-stack application by developing a blog application— a clone of medium.com , called Conduit. Conduit has the following general functionality:
Cyphertext-only attack on the joint-transform-correlator-based optical encryption: experimental demonstration

A cyphertext-only attack (COA) on a joint transform correlator encryption system is proposed. Under the proposed COA scheme, the energy spectral density of plaintext can be calculated with cyphertext, and then plaintext information can be recovered by using a phase retrieval algorithm such as the hybrid input-output algorithm. We also numerically and experimentally demonstrate the COA to verify feasibility of the proposed technique.
Visual Object Tracking Robust to Illumination Variation Based on Hyperline Clustering

Color histogram-based trackers have obtained excellent performance against many challenging situations. However, since the appearance of color is sensitive to illumination, they tend to achieve lower accuracy when illumination is severely variant throughout a sequence. To overcome this limitation, we propose a novel hyperline clustering based discriminant model, an illumination invariant model that is able to distinguish the object from its surrounding background. Furthermore, we exploit this model and propose an anchor based scale estimation to cope with shape deformation and scale variation. Numerous experiments on recent online tracking benchmark datasets demonstrate that our approach achieve favorable performance compared with several state-of-the-art tracking algorithms. In particular, our approach achieves higher accuracy than comparative methods in the illumination variant and shape deformation challenging situations.
Effect of Phase Error in Phase-Shifting Interferometer

Phase-shifting interferometry is widely used because it can measure phase with high accuracy. Changing optical path length with a PZT transducer and changing frequency of the optical source with a laser diode (LD) are two common methods to apply the desired phase shift between the arms in the interferometer. In any case, however, it is not easy to apply the desired shift accurately, and if not, measurement errors occur. In order to reduce the errors, the effect of the phase shift error has been analyzed numerically especially in the case with LD.
Development of an Improved Schema Entropy and Interface Complexity Metrics

Extensible Markup Language is a mark-up language that defines a set of rules for encoding documents in a format that is both human-readable and machine-readable for managing information contained in the schema documents and exchanging wide variety of elements class of data to reflect understandability and reusability. The developed schema entropy and interface complexity metrics are based on the concept from DTD metrics to measure complexity of schema on similarly structured elements, distinct structured elements and their occurrences using the Number of Attributes (NOA), Number of Equivalence Class (NOC), Frequency Occurrence of the class (FOC) and Number of Elements (NOE). A higher value of ISEM and IICM tend to a degree of high flexibility and reusability quality. The empirical validation of the metrics is applied on 40 WSDL schema files. A comparison with similar measures is also performed. The proposed ISEM and IICM metrics validated practically and theoretically. The comparative study proves the robustness of the metrics and performed better in terms of reusability and reducing lengthy code. The statistical analysis of the study showed a significant and linear relationship; and high degree of correlation.
The Current State of Technical Editing Research and the Open Questions

This chapter consists of a detailed literature review that explores the current state of technical editing research, connects that research to technical editing pedagogy, and considers the open questions exposed by that research. The literature review emphasizes empirical research published since 2000 in peer-reviewed technical communication journals, yet also looks at older publications that worked to define the technical editing field and its practices. Though the term technical editing still lacks an established definition, technical editing is a core class in technical communication programs. Technical editing curricula and textbooks have largely been shaped by technical editing lore and personal experience, but programmatic knowledge is developing with each empirical study. Recent studies have examined editorial comments, electronic editing, editing tests, workplace editing practices, and technical editing as quality control. Empirical research is needed on technical editing as it intersects with international audiences, artificial intelligence, and emerging publishing technologies.