Reviewerly/exHarmony huggingface dataset:
An empirical study of data visualization techniques in PACS design

The paper presents an empirical study of multidimensional visualization techniques. The study is motivated by the problem of decision making in PACS (Picture Archiving and Communications System) design. A comprehensive survey of visualizations used in literature is performed and these survey results are then used to produce the final set of considered visualizations: tables (as control), scatterplots, parallel coordinates, and star plots. An electronic testing tool is developed to present visualizations to three sets of experimental subjects in order to determine which visualization technique allows users to make the correct decision in a sample decision making problem based on real-world data. Statistical analysis of the results demonstrates that visualizations show better results in decision support than tables. Further, when number of dimensions is large, 2D parallel coordinates show the best results in accuracy. The contribution of the presented research operates on two levels of abstraction. On the object level, it provides useful data regarding the relative merits of visualization techniques for the considered narrow use-case, which can then be generalized to other similar problem sets. On the meta level above, it contributes an enhanced methodology to the area of empirical visualization evaluation methods.
Post-processing minimal joint observables

A finite set of quantum observables (positive operator valued measures) is called compatible if these observables are marginals of a some observable, called a joint observable of them. For a given set of compatible observables, their joint observable is in general not unique and it is desirable to take a minimal joint observable in the post-processing order since a less informative observable disturbs the system less. We address the question of the minimality of finite-outcome joint observables and prove that any joint observable is lower bounded by a minimal joint observable in the post-processing order. We also give characterizations of the minimality of a joint observable that can be checked by finite-step algorithms and apply them to the case of non-commuting dichotomic qubit observables.
Dot-Pipe: an S3 Extensible Pipe for R

Pipe notation is popular with a large league of R users, with magrittr being the dominant realization.However, this should not be enough to consider piping in R as a settled topic that is not subject to further discussion, experimentation, or possibility for improvement.To promote innovation opportunities, we describe the wrapr R package and "dot-pipe" notation, a well behaved sequencing operator with S3 extensibility.We include a number of examples of using this pipe to interact with and extend other R packages.
A Generic Framework for Accountable Optimistic Fair Exchange Protocol

Optimistic Fair Exchange protocol was designed for two parties to exchange in a fair way where an arbitrator always remains offline and will be referred only if any dispute happens. There are various optimistic fair exchange protocols with different security properties in the literature. Most of the optimistic fair exchange protocols satisfy resolution ambiguity where a signature signed by the signer is computational indistinguishable from the one resolved by the arbitrator. Huang et al. proposed the first generic framework for accountable optimistic fair exchange protocol in the random oracle model where it possesses resolution ambiguity and is able to reveal the actual signer when needed. Ganjavi et al. later proposed the first generic framework in the standard model. In this paper, we propose a new generic framework for accountable optimistic fair exchange protocol in the standard model using ordinary signature, convertible undeniable signature, and ring signature scheme as the underlying building blocks. We also provide an instantiation using our proposed generic framework to obtain an efficient pairing-based accountable optimistic fair exchange protocol with short signature.
Literasi Informasi dan Literasi Media

This article discusses information literacy and media literacy.Globalization creates a borderless or unlimited world so that information can be spread widely without a filter that results in a mixture of hoax or fake information.In determining diverse information needs in the digital era, people are asked to be more selective, sensitive and think critically about information that is spread randomly in the digital world.Literacy is a must-have capability for information civilizations that now live in an era of all technology.The ability to identify, analyze and evaluate information and media in finding information, content that is accurate, accurate, credible and up-to-date.
Redio: Accelerating Disk-Based Graph Processing by Reducing Disk I/Os

Disk-based graph systems store part or all of graph data on external devices like hard drives or SSDs, achieving scalability without excessive hardware. However, massive expensive disk I/Os remain the major performance bottleneck of disk-based graph processing. In this paper, we propose Redio, a new approach to accelerating disk-based graph processing by reducing disk I/Os. First, Redio observes that it is feasible to accommodate all vertex states in main memory and this can eliminate almost all vertex-related disk I/Os. Second, Redio introduces a dynamic selective scheduling scheme to identify inactive edges in each iteration and skip them when and only when such skipping can bring performance benefit. To improve its effectiveness, Redioin corporates a compact edge storage to improve data locality and an indexed bitmap to minimize its memory and computation overheads. We have implemented a single-node prototype for Redio under the edge-centric computation model. Extensive experiments show that Redio consistently outperforms well-known edge-centric disk-based systems in all experiments, delivering an average speedup of <inline-formula><tex-math notation="LaTeX">$4.33\times$</tex-math></inline-formula> on HDDs and <inline-formula><tex-math notation="LaTeX">$5.33\times$</tex-math></inline-formula> on SSDs over the fastest among them (i.e., GridGraph). Experimental results also show that Redio delivers an average speedup of <inline-formula><tex-math notation="LaTeX">$3.13\times$</tex-math></inline-formula> on HDDs and <inline-formula><tex-math notation="LaTeX">$1.28\times$</tex-math></inline-formula> on SSDs over the fastest among representative vertex-centric disk-based systems (i.e., FlashGraph).
Efficient and robust segmentation and correction model for medical images

Accurate segmentation of medical images plays a very important role in clinical diagnosis so that the segmentation technology for medical images attracts more and more attention. However, most medical images usually suffer from severe intensity inhomogeneity and make accurate segmentation difficult. In this study, the authors propose an efficient and robust active contour model for simultaneous image segmentation and correction. The proposed model not only can accurately segment images with severe intensity inhomogeneity and serious noise but also can eliminate the intensity varying information to get the homogeneous correction images. They first present the level set formulation of the two-phase model, which is then extended to the multi-phase formulation. The split Bregman method is applied to efficiently minimise the proposed energy functionals. The proposed model is tested with lots of synthetic images and medical images with promising results. Experimental results demonstrate that the proposed model can accurately segment and correct the inhomogeneous images with serious noise. Quantitative comparison results of the proposed model and other models illustrate the proposed model is more accurate and more efficient. What's more, the proposed model not only is insensitive to the initial contour, but also is robust to the noise.
Temporal Pattern Specifications for Self-Adaptive Requirements

Background: Systems whose requirements change at a rate that necessitates adaptation without human intervention are called self-adaptive systems, and they have the ability to adjust their behavior autonomously at run-time in response to their environment’s evolution. Samples of applications that require self-adaptation include Smart home systems and environmental monitoring. However, self-adaptivity is often constructed in an ad-hoc manner. Methods: In this paper, the authors present a pattern-based specification language for self-adaptive systems. Its semantics are presented in terms of fuzzy logic. Thus, enabling a meticulous processing of requirements, in order to permit the formulation of self-adaptive requirements accurately, thereby facilitates the design of systems that are flexible and responsive to adaptation in a systematic manner. Results: To show the applicability and effectiveness of our language, the authors apply it to two case studies. One case study reviews the Smart fridge in ambient assisted living and the second case study is focused on an ambulance dispatching system using a developed support tool.
ANALISIS KESALAHAN BERBAHASA INDONESIA PADA KARANGAN EKSPOSISI SISWA KELAS X MIPA (STUDI KASUS DI SMA NEGERI 4 SURAKARTA)

&lt;em&gt;This study aims to describe and explain the form of language errors in the field&lt;strong&gt; &lt;/strong&gt;of phonology, morphology, and syntax in the exposition essay of the student of X MIPA SMA N 4 Surakarta and describe the causes of language errors and efforts that can be done by teachers and students so that the Indonesian language error in the exposition essay by the students of X MIPA SMA N 4 Surakarta can be minimized. The research uses qualitative method with case study type. Research data in the form of Indonesian language error obtained through the exposition essay by the student of X MIPA and the data source used are documents and informants. Data collection technique conducted through observation, interviews, dan test. Data validity test used is triangulation method, informant review, dan intrarater. The result of this research are description of: (1) forms of language errors in the phonological field include capitalization misuse, misuse of punctuation, particle error, rewrite error, typographic error, and typing error; (2) the form of language error in the morphological field include mistakes of preposition, mistake of word formation, and error of pleonasm; (3) the form of language error in the syntax field include the phrase structure dan sentence structure; and (4) the factors causing the Indonesian language error not only comes from the students but also because of teacher and efforts that can be done to minimize language errors.&lt;/em&gt;
Visual Focus of Attention and Spontaneous Smile Recognition Based on Continuous Head Pose Estimation by Cascaded Multi-Task Learning

Multi-person Visual focus of attention (M-VFOA) and spontaneous smile (SS) recognition are important for persons’ behavior understanding and analysis in class. Recently, promising results have been reported using special hardware in constrained environment. However, M-VFOA and SS remain challenging problems in natural and crowd classroom environment, e.g. various poses, occlusion, expressions, illumination and poor image quality, etc. In this study, a robust and un-invasive M-VFOA and SS recognition system has been developed based on continuous head pose estimation in the natural classroom. A novel cascaded multi-task Hough forest (CM-HF) combined with weighted Hough voting and multi-task learning is proposed for continuous head pose estimation, tip of the nose location and SS recognition, which improves accuracies of recognition and reduces the training time. Then, M-VFOA can be recognized based on estimated head poses, environmental cues and prior states in the natural classroom. Meanwhile, SS is classified using CM-HF with local cascaded mouth-eyes areas normalized by the estimated head poses. The method is rigorously evaluated for continuous head pose estimation, multi-person VFOA recognition, and SS recognition on some public available datasets and real-class video sequences. Experimental results show that our method reduces training time greatly and outperforms the state-of-the-art methods for both performance and robustness with an average accuracy of 83.5% on head pose estimation, 67.8% on M-VFOA recognition and 97.1% on SS recognition in challenging environments.
Testing Halfspaces over Rotation-Invariant Distributions

We present an algorithm for testing halfspaces over arbitrary, unknown rotation-invariant distributions. Using random examples of an unknown function f, the algorithm determines with high probability whether f is of the form f(x) = sign(∑i ωixi – t) or is ∊-far from all such functions. This sample size is significantly smaller than the well-known requirement of Θ(n) samples for learning halfspaces, and known lower bounds imply that our sample size is optimal (in its dependence on n) up to logarithmic factors. The algorithm is distribution-free in the sense that it requires no knowledge of the distribution aside from the promise of rotation invariance. To prove the correctness of this algorithm we present a theorem relating the distance between a function and a halfspace to the distance between their centers of mass, that applies to arbitrary distributions.
THORS: An Efficient Approach for Making Classifiers Cost-Sensitive

In this paper, we propose an effective THresholding method based on the Order Statistic, called THORS, to convert an arbitrary scoring-type classifier, which can induce a continuous cumulative distribution function of the score, into a cost-sensitive one. The procedure uses the order statistic to find an optimal threshold for classification, requiring almost no knowledge of the classifier itself. Unlike common data-driven methods, we analytically show that the THORS has theoretical guaranteed performance, theoretical bounds for the costs, and low-time complexity. Coupled with empirical results on several real-world data sets, we argue that the THORS is the preferred cost-sensitive learning technique.
A Novel Convolutional Neural Network for Facial Expression Recognition

Facial expression recognition is becoming a hot topic due to its wide applications in computer vision research fields. Traditional methods adopt hand-crafted features combined with classifiers to achieve the recognition goal. However, the accuracy of these methods often relies heavily on the extracted features and the classifier’s parameters, and thus cannot get good result with unseen data. Recently, deep learning, which simulates the mechanism of human brain to interpret data, has shown remarkable results in visual object recognition. In this paper, we present a novel convolutional neural network which consists of local binary patterns and improved Inception-ResNet layers for automatic facial expression recognition. We apply the proposed method to three expression datasets, i.e., the Extended Cohn-kanade Dataset (CK+), the Japanese Female Expression Database (JAFFE), and the FER2013 Dataset. The experimental results demonstrate the feasibility and effectiveness of our proposed network.
Chaotic Quantum Double Delta Swarm Algorithm Using Chebyshev Maps: Theoretical Foundations, Performance Analyses and Convergence Issues

The Quantum Double Delta Swarm (QDDS) Algorithm is a networked, fully-connected novel metaheuristic optimization algorithm inspired by the convergence mechanism to the center of potential generated within a single well of a spatially colocated double–delta well setup. It mimics the wave nature of candidate positions in solution spaces and draws upon quantum mechanical interpretations much like other quantum-inspired computational intelligence paradigms. In this work, we introduce a Chebyshev map driven chaotic perturbation in the optimization phase of the algorithm to diversify weights placed on contemporary and historical, socially-optimal agents’ solutions. We follow this up with a characterization of solution quality on a suite of 23 single–objective functions and carry out a comparative analysis with eight other related nature–inspired approaches. By comparing solution quality and successful runs over dynamic solution ranges, insights about the nature of convergence are obtained. A two-tailed t-test establishes the statistical significance of the solution data whereas Cohen’s d and Hedge’s g values provide a measure of effect sizes. We trace the trajectory of the fittest pseudo-agent over all iterations to comment on the dynamics of the system and prove that the proposed algorithm is theoretically globally convergent under the assumptions adopted for proofs of other closely-related random search algorithms.
Design of dividing reconfigurable phase mask by random size for optical security system

In this paper, we propose an algorithm for designing structured phase mask in random sized segmentation, which is the security key in optical cryptosystem. A scheme for the generation, store and encoding this mask is demonstrated. We present the numerical simulation results of the proposed phase masks employed in the double random phase encoding system. To show the improvements of this structured phase mask (SPM) over the random phase mask (RPM), the encryption and decryption behaviors of the SPM and RPM are compared. The structural parameters sensitivity simulations of the key were also performed.
Security risk analysis of bring your own device system in manufacturing company at Tangerang

Nowadays information system has become popular used for help effectiveness and efficiency operation on a company. Bring Your Own Device (BYOD) system is a growing trend in corporate environment, where employees could access the system from anywhere. BYOD system is system information development using some technology like a Virtual Private Networks (VPN) or using some application to make the client on outside network office can access to inside networks with remote system. The remote system has strength to help employees working anywhere and anytime, that could make some issue for a security thing. The security issue that can be happen is unauthorized access and lost some important of company information. XYZ company as a manufacturing company in Tangerang, Indonesia has been used BYOD system in their company. They want to improve the security of the system with do risk analysis, with the aim to protect the internal data. The risk analysis use Cybersecurity Framework NIST will assist organizations to understand the risk of BYOD system. The analysist results obtained by the use of cybersecurity analysis on BYOD system in XYZ company are found some improvement need to develop in terms of security system recommended. According to the stages of respond with the analysis using Cybersecurity NIST framework and ISO/IEC 27002:2013.
Enhancing 3D Modeling with Augmented Reality in an After-school Engineering Program (Work in Progress)

for studying cognition.
Full-Vector Gradient for Multi-Spectral or Multivariate Images

Gradient extraction is important for a lot of metrological applications, such as control quality by vision. In this paper, we propose a full-vector gradient for multi-spectral sensors. The full-vector gradient extends Di Zenzo expression to take into account the non-orthogonality of the acquisition channels thanks to a Gram matrix. This expression is generic and independent from channel count. Results are provided for a color and a multi-spectral snapshot sensor. Then, we show the accuracy improvement of the gradient calculation by creating a dedicated objective test and from real images.
Inverse Abstraction of Neural Networks Using Symbolic Interpolation

Neural networks in real-world applications have to satisfy critical properties such as safety and reliability. The analysis of such properties typically requires extracting information through computing pre-images of the network transformations, but it is well-known that explicit computation of pre-images is intractable. We introduce new methods for computing compact symbolic abstractions of pre-images by computing their overapproximations and underapproximations through all layers. The abstraction of pre-images enables formal analysis and knowledge extraction without affecting standard learning algorithms. We use inverse abstractions to automatically extract simple control laws and compact representations for pre-images corresponding to unsafe outputs. We illustrate that the extracted abstractions are interpretable and can be used for analyzing complex properties.
Disjunctive Normal Form for Multi-Agent Modal Logics Based on Logical Separability

Modal logics are primary formalisms for multi-agent systems but major reasoning tasks in such logics are intractable, which impedes applications of multi-agent modal logics such as automatic planning. One technique of tackling the intractability is to identify a fragment called a normal form of multiagent logics such that it is expressive but tractable for reasoning tasks such as entailment checking, bounded conjunction transformation and forgetting. For instance, DNF of propositional logic is tractable for these reasoning tasks. In this paper, we first introduce a notion of logical separability and then define a novel disjunctive normal form SDNF for the multiagent logic Kn, which overcomes some shortcomings of existing approaches. In particular, we show that every modal formula in Kn can be equivalently casted as a formula in SDNF, major reasoning tasks tractable in propositional DNF are also tractable in SDNF, and moreover, formulas in SDNF enjoy the property of logical separability. To demonstrate the usefulness of our approach, we apply SDNF in multi-agent epistemic planning. Finally, we extend these results to three more complex multi-agent logics Dn, K45n and KD45n.
Orderly Subspace Clustering

Semi-supervised representation-based subspace clustering is to partition data into their underlying subspaces by finding effective data representations with partial supervisions. Essentially, an effective and accurate representation should be able to uncover and preserve the true data structure. Meanwhile, a reliable and easy-to-obtain supervision is desirable for practical learning. To meet these two objectives, in this paper we make the first attempt towards utilizing the orderly relationship, such as the data a is closer to b than to c, as a novel supervision. We propose an orderly subspace clustering approach with a novel regularization term. OSC enforces the learned representations to simultaneously capture the intrinsic subspace structure and reveal orderly structure that is faithful to true data relationship. Experimental results with several benchmarks have demonstrated that aside from more accurate clustering against state-of-the-arts, OSC interprets orderly data structure which is beyond what current approaches can offer.
Simultaneous nonparametric regression in RADWT dictionaries

A new technique for nonparametric regression of multichannel signals is presented.The technique is based on the use of the Rational-Dilation Wavelet Transform (RADWT), equipped with a tunable Q-factor able to provide sparse representations of functions with different oscillations persistence.In particular, two different frames are obtained by two RADWT with different Q-factors that give sparse representations of functions with low and high resonance.It is assumed that the signals are measured simultaneously on several independent channels and that they share the low resonance component and the spectral characteristics of the high resonance component.Then, a regression analysis is performed by means of the grouped lasso penalty.Furthermore, a result of asymptotic optimality of the estimator is presented using reasonable assumptions and exploiting recent results on group-lasso like procedures.Numerical experiments show the performance of the proposed method in different synthetic scenarios as well as in a real case example for the analysis and joint detection of sleep spindles and K-complex events for multiple electroencephalogram (EEG) signals.