Reviewerly/exHarmony huggingface dataset:
On Modal Logics of Model-Theoretic Relations

Given a class $$\mathcal {C}$$ of models, a binary relation $$\mathcal {R}$$ between models, and a model-theoretic language L, we consider the modal logic and the modal algebra of the theory of $$\mathcal {C}$$ in L where the modal operator is interpreted via $$\mathcal {R}$$ . We discuss how modal theories of $$\mathcal {C}$$ and $$\mathcal {R}$$ depend on the model-theoretic language, their Kripke completeness, and expressibility of the modality inside L. We calculate such theories for the submodel and the quotient relations. We prove a downward Löwenheim–Skolem theorem for first-order language expanded with the modal operator for the extension relation between models.
Optimizing FHEW With Heterogeneous High-Performance Computing

The latest implementation of the fully homomorphic encryption algorithm (FHEW), FHEW-V2, takes about 0.12 s for a bootstrapping on a single-node computer. It seems much faster than the previous implementations. However, the 30-bit homomorphic addition requires 270 times of bootstrapping; plus those spent on key generation, the total elapsed time climbs to 55 seconds, which is unacceptable. In this article, we reveal how to further optimize FHEW-V2 by focusing on efficiently constructing homomorphic full adders. We tackle inefficiency in FHEW-V2 by massive efforts: First, we explore FHEW-V2 and locate hotspots; second, we leverage the heterogeneous parallel computing model of multicore CPU and GPUs to remove the hotspots to improve performance. The empirical results show that a 30-bit homomorphic addition is completed in 23.8753 s after optimization, gaining an overall speedup of 2.2845; and a 6-bit homomorphic multiplication costs 25.8438, gaining an overall speedup of 2.2435. The 2.2845 speedup is a rough integration of a 13.248 speedup for the key generation and a 1.672 speedup for the bootstrapping; the 2.2435 speedup is a rough integration of the same key generation and a 1.675 speedup for the bootstrapping. We also reveal the strengths and weaknesses of FHEW-V2 by comparing it with a state-of-the-art somewhat homomorphic encryption algorithm, microsoft's simple encrypted arithmetic library (SEAL).
Drama patterns: Seeing the patterns from within

This report summarizes a focus group on drama patterns we organized at EuroPLoP 2019. Apart from the organizers, the focus group attracted eight participants, who together built a short drama play entitled The Real Red Riding Hood (created by Aleksandra Vranić). The play went through three settings with the final setting involving three drama patterns and one fundamental property (as defined by Alexander): Thoughts Reflecting Environment, Reversed Advantage, Disbelieved Identity, and Echoes. In addition, the participants tried out a name learning game based on the Thoughts Reflecting Environment pattern. The final setting was performed at the conference banquet without any further rehearsals (the focus group took only 75 minutes), followed by numerous informal discussions and reflections on how drama patterns could be beneficial for achieving a better understanding of the concept of a pattern, pattern composition, and pattern language, and for education in general. The focus group once more confirmed the effectiveness and efficiency of drama patterns that has been observed so far.
Source Camera Identification for Online Social Network Images Using Texture Feature

Nowadays, digital images are easily shared through social media and it common amongst internet users. It was a convenient way to share a moment and communicate with people all over the worlds through social media on the internet. However, this has caused the increasing number of crimes involving digital images in social media. It is well known that each digital image that passes through online social networks (OSNs) is explicitly modified by Web 2.0 tools. Thus, it is challenging for authorities to probe further, including identifying the source of the digital images. Considering this limitation, an alternative method to identify source camera based on the texture feature for OSNs images is proposed. This technique uses texture feature characteristics, namely, Gray Level Co-Occurrence Matrix (GLCM) and Gray Level Run-Length Matrix (GLRLM). Original and OSNs images were tested to determine whether the proposed method is robust for both image types and gives higher accuracy than previous methods. Four types of camera models were used in this research. The results prove that the method tested in this study is accurate with an average accuracy of 97.00% and 99.59% for original and OSNs images, respectively, and is capable to read up to 600 images.
Validity and Reliability of the Sense of Belonging Instrument with Online Graduate Nursing Students

Student retention is a major focus for higher education. Studies show that retention is related to a sense of belonging. The Sense of Belonging Instrument (SOBI) is a valid and reliable instrument for undergraduate students. However, the validity and reliability of the instrument has not been established relative to graduate nursing students. The current descriptive study establishes content validity and reliability of the SOBI. Content validity was established by having three graduate school nursing faculty evaluate each item. There was a full agreement among the faculty that each item reflected the concept of a sense of belonging and that the instrument was comprehensive. For reliability, the SOBI was administered to 315 graduate nursing students and 112 participants completed the instrument. Reliability was established by the use of Cronbach's Alpha. The resulting instrument had a high reliability of 0.903. Future studies need to be implemented to determine what methods are most effective in creating a sense of belonging. Furthermore, the SOBI can be used to analyze which approaches are most effective, and whether sense of belonging enhances retention.
The Cambridge Grammar of Classical Greek. A new reference grammar for Classical Greek: aims and principles

We are grateful to the editor of JCT for offering us the opportunity of outlining our views on the need for a new Greek reference grammar, to discuss our methodological principles, and to offer some thoughts on how the book may be useful as a teaching resource.
Discovering Opioid Use Patterns From Social Media for Relapse Prevention

The United States is experiencing an unprecedented opioid crisis. Through a multidisciplinary analytic perspective, we characterize opioid addiction behavior patterns by analyzing opioid groups from Reddit.com—including modeling online discussion topics, analyzing text co-occurrence and correlations, and identifying the emotional states of people with opioid use disorder.
10° Off-Axis Tensile Testing of Carbon Fiber Reinforced Polymers Using Digital Image Correlation

The current work describes the use of DIC (digital image correlation) for full-field strain and deformation response while performing 10° off-axis tensile tests on rigidly clamped unidirectionally carbon fiber reinforced (CFRP-UD) coupon specimens with a nominal reinforcing fraction of 50 volume percent. Off-axis testing of anisotropic materials produces a nonuniform state of stress and strain when the ends are rigidly clamped and bending moments cannot be eliminated by free rotation. Under this configuration, the application of constant end displacements induces shearing forces and bending couples, which result in the nonuniform deformation. The extension-shear coupling compliance causes the coupon to deform into an S-shape, which is qualitatively visualized using DIC. Quantitative photomechanical investigations of vertical and horizontal displacements of the coupon are compared to approximate analytical solutions originally derived in the 1960s. Additionally, a description of deformation and fracture mechanisms using the DIC calculated strains is provided. In addition to the shear strain in the specimen coordinate axis, the maximum shear strain (used to quantify the shear modulus and the strain to fracture) and normal strain (transverse to fiber orientation) are calculated via strain transformation equations and visualized.
Online Multi-Task Clustering for Human Motion Segmentation

Human motion segmentation in time space becomes attractive recently due to its wide range of potential applications on action recognition, event detection, and scene understanding tasks. However, most existing state-of-the-arts address this problem upon an offline and single-agent scenario, while there are a lot of urgent requirements to segment videos captured from multiple agents for real-time application (e.g., surveillance system). In this paper, we propose an Online Multi-task Clustering (OMTC) model for an online and multi-agent segmentation scenario, where each agent corresponds to one task. Specifically, a linear autoencoder framework is designed to project motion sequences into a common motion-aware space across multiple collaborating tasks, while the decoder obtains motion-aware representation of each task via a temporal preserved regularizer. To tackle distribution shifts problem between each pair of tasks, the task-specific projections are further proposed to align representation across the motion segmentation tasks. By this way, significant motion knowledge can be shared among multiple tasks, and the temporal data structures are also well preserved. For the model optimization, an efficient and effective online optimization mechanism is derived to solve the large-scale formulation in real-time applications. Experiment results on Keck, MAD and our collected human motion datasets demonstrate the robustness, high-accuracy and efficiency of our OMTC model.
A Comparative Study of the Stability of Filter based Feature Selection Algorithms

Feature selection is an important step prior to classification stage of machine learning, pattern recognition and data mining problems for addressing the high dimensionality of the data. It removes irrelevant and redundant features which lead to simplify classification process and improve accuracy. Several feature selection algorithms have been proposed so far and quality of the selected feature subset varies from algorithm to algorithm. One of the measures for assessing the quality of a feature selection algorithm is its stability. Stability refers to the robustness of the selected feature set to small changes in the training set or set of various parameters of the algorithm. In this work, a comparative study of the stability of several well-known filter based feature selection algorithms, producing ranked feature sub set, has been done. Fifteen benchmark datasets from the UCI repository have been used for simulation experiments. Three types of stability measures, index-based, rank-based and weight based are used to evaluate the stability of feature selection algorithms. Simulation results demonstrate that for most of the datasets, JMD-based feature selection algorithm exhibits more stability irrespective of all types of stability measures. It is also observed that Relief shows the least stability.
Abrupt Scene Change Detection Using Spatiotemporal Regularity of Video Cube

In this paper, we propose the detection method of abrupt scene change using spatial as well as spatiotemporal frames of video cube. Most of the methods use either intensity or motion of pixels for the scene change detection methodology. Unlike to the existing methods, both the intensity and flow vector of video frames are used simultaneously in this paper to propose a general abrupt scene change detection method. For a spatial frame, flow energy function is used for detection. Flow energy function, defined by the spatiotemporal regularity flow model, is the combinatorial form of intensity and flow vectors of the frames. In the spatio-temporal frames, abrupt scene change appears as a vertical line which is detected by the edge detection method. Combined results of spatial and the spatio-temporal frames provide the location of scene change. The proposed method detects almost all the locations of scene change with negligible false detection.
The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing

Abstract This paper presents the ‘NoisyOffice’ database. It consists of images of printed text documents with noise mainly caused by uncleanliness from a generic office, such as coffee stains and footprints on documents or folded and wrinkled sheets with degraded printed text. This corpus is intended to train and evaluate supervised learning methods for cleaning, binarization and enhancement of noisy images of grayscale text documents. As an example, several experiments of image enhancement and binarization are presented by using deep learning techniques. Also, double-resolution images are also provided for testing super-resolution methods. The corpus is freely available at UCI Machine Learning Repository. Finally, a challenge organized by Kaggle Inc. to denoise images, using the database, is described in order to show its suitability for benchmarking of image processing systems.
A Sparse Approach for Identification of Signal Constellations Over Additive Noise Channels

Identification of unknown linear modulations over arbitrary additive noise channels is addressed within the framework of sparse linear regression. A regularized least squares problem with a sparsity inducing penalty is formulated to estimate the distribution of the transmitted symbols, which complete characterizes the underlying signal constellation. Separable and iterative algorithms that deliver reduced computational complexity are obtained based on the majorization-minimization framework. The proposed method can be employed to construct a modulation dictionary tailored to the target communications system before performing hypothesis testing-based classification.
The Utilization of Smartphone Communication Technology the as Digital Literacy Learning School Instruments in 4.0 Era

Abstract Numbers of institutes report that students’ competency literacy on numeracy, readings and science is low. The presence of digital technology such as computers and smartphones has become part of the students’ daily life. However, digital technology has not provided benefits for students to improve their competence. The challenges of education in the industrial era 4.0 are changes in learning ways, thinking patterns, actions on creative innovation that students have competitiveness in global market. The aim of the study is to explore: the educators’ capability in era 4.0, teaching materials preparation, and digital literacy technical constraints. Explorative methods with descriptive analysis and test. Data is collected by open interviews, in-depth interviews and data reviews. Random samples are limited to 20 teachers and 40 students in Semarang. The acceleration of digital literacy is a necessity, namely data literacy, technology literacy; human literacy has not shown optimal utilization. Through Android-based learning automation and the like is necessary to be applied both media and learning resources. The media utilized can increase digital literacy total score of more than 78 of the indicators specified, besides that the use of this instrument is more 1efficient at around 60%. Optimization can be achieved by-design by teachers as facilitators.
Digital Touch Ethics and Values

This chapter examines key ethical considerations and challenges of designing and researching touch technologies, with a focus on incorporating ethical touch sensitivities and values into digital touch communication. We discuss the difficulty of researching and designing ethically in the context of an emerging technological landscape, as reflected in wider HCI ethics debate. The chapter then explores the central role of the human body as site for digital touch communication, before focusing on key challenges around trust, control, consent, and tactile data. In line with preceding chapters, we argue that digital touch practices are part of, and impact on, wider social relations and communications. The kinds of touch practices and relations designed into touch technologies bring with them implications for power relations and social cohesion, and it is these wider processes that digital touch design is able to – at least in parts – anticipate and shape. We close with a summary of key points and their implications for research and design.
Training Functional Link Neural Network with Ant Lion Optimizer

Functional Link Neural Network (FLNN) has becoming as an important tool used in machine learning due to its modest architecture. FLNN requires less tunable weights for training as compared to the standard multilayer feed forward network such as Multilayer Perceptron (MLP). Since FLNN uses Backpropagation algorithm as the standard learning algorithm, the method however prone to get trapped in local minima which affect its performance. This paper proposed the implementation of Ant Lion Algorithm as learning algorithm to train the FLNN for classification tasks. The Ant Lion Optimizer (ALO) is the metaheuristic optimization algorithm that mimics the hunting mechanism of antlions in nature. The result of the classification made by FLNN-ALO is compared with the standard FLNN model to examine whether the ALO learning algorithm is capable of training the FLNN network and improve its performance. From the result achieved, it can be seen that the implementation of the proposed learning algorithm for FLNN performs the classification task quite well and yields better accuracy on the unseen data.
Yun: A High-Performance Container Management Service Based on OpenStack

As an emerging technology in cloud computing, Docker is becoming increasingly popular due to its fast speed, high efficiency and portability. The integration of Docker with OpenStack has been a hot topic in research and industrial areas, e.g., in the emulation platform for evaluating cyberspace security technologies. This paper implements a high-performance container management service called Yun based on OpenStack. Yun interacts with OpenStack's services and manages the lifecycle of the container through the Docker Engine to achieve the integration of OpenStack and Docker. Yun introduces a new NUMA-aware and resource utilization-aware scheduling model to improve the performance of Docker and balance the load of computing resources. Experiments show that, compared to traditional methods, Yun not only achieves the integration of OpenStack and Docker but also has the advantages of high performance in terms of CPU and memory under resource competition and achieves load balancing.
An Efficient Unified Approach Using Demonstrations for Inverse Reinforcement Learning

A reinforcement learning (RF) agent is always equipped with a designed reward function to correct policies for optimal decision making through interactions with an environment. However, it is difficult to design a reward function appropriate for complex RF problems. To solve this difficulty, the inverse RF (IRL) is introduced to provide an efficient way to design a reward function based on input derived from knowledgeable experts. In the IRL, experts provide demonstrations so that the agents can imitate the behaviors accordingly. However, even incorrect demonstrations have merits, some of which are similar to correct ones, so as that the agents with these clues can endeavor to avoid the occurrence of that behavior. This article introduces an IRL method which considers two types of demonstrations, correct and incorrect, in function approximation of a reward function. Given the clues from two opposite demonstrations, agents can iteratively approximate a reward function that can guide them to like expert’s correct demonstrations and also, prevent them from making the same mistakes as the expert did. These incorrect demonstrations provide agents with some guidelines to avoid erroneous motions in the initial phase. Two simulated tasks, a labyrinth and robot soccer games are conducted to validate the proposed method. The simulation results show that the proposed method can achieve the objectives of generating an appropriate reward function to accomplish apprentice learning with an efficient learning time in IRL.
An overview of various enhancements of DENCLUE algorithm

Clustering is one of the main data mining methods for knowledge discovery. The clustering is an exploratory data analysis technique that categorizes different data objects into similar groups, named clusters. Density-based clustering defines clusters as dense regions that are separated by low dense regions. The DENCLUE (DENsity CLUstEring) is a robust density-based algorithm for discovering clusters with arbitrary shapes and sizes. Although its efficiency, the DENCLUE suffers from the following issues: (1) It is sensitive to the values of its parameters. (2) It fails to discover clusters with highly varying densities. (3) It may require large computation time for clustering large datasets. Several research articles attempted to enhance the performance of the DENCLUE algorithm to overcome the mentioned issues. This research surveys the proposed enhancements of the DENCLUE algorithm concerning their main contribution, input parameters, and evaluation measures. The research aims to serve as a base for future enhancements of the DENCLUE algorithm.
Real-Time Facial Motion Capture Using RGB-D Images Under Complex Motion and Occlusions

We present a technique for capturing facial performance in real time using an RGB-D camera. Such method can be applied to face augmentation by leveraging facial expression changes. The technique is able to perform both 3D facial modeling and facial motion tracking without the need of pre-scanning or training for a specific user. The proposed approach builds on an existing method that we refer as FaceCap, which uses a blendshape representation and a Bump image for tracking facial motion and capturing geometric details. The original FaceCap algorithm fails in some scenarios with complex motion and occlusions, mainly due to problems in the face detection and tracking steps. FaceCap also has problems with the Bump image filtering step that generates outliers, causing more distortion on the 3D augmented blendshape. In order to solve these problems, we propose two refinements: (a) a new framework for face detection and landmark localization based on the state-of-the-art methods MTCNN and CE-CLM, respectively; and (b) a simple but effective modification in the filtering step, removing reconstruction failures in the eye region. Experiments showed that the proposed approach can deal with unconstrained scenarios, such as large head pose variations and partial occlusions, while achieving real-time execution.
The three-step refactoring detector pattern

Developing a tool that provides support for different refactorings, through a set of refactoring detectors which identify opportunities for source code improvements, is not easy. Our experience in developing such a tool for refactoring object-oriented software revealed the Three-Step Refactoring Detector pattern. The main idea behind the pattern is to develop an extensible hierarchy of refactoring detectors, with respect to a general three-step refactoring detection process. The proposed pattern facilitates the expansion of the hierarchy with new refactoring detectors and enables the reuse of existing refactoring detectors, provided by third party developers. Concerning maintainability, the pattern promotes the development of simple, clean and technology independent refactoring detectors. We have used the pattern for the development of 11 different refactoring detectors in the context of our tool. The pattern has not been observed in other contexts. However, the usage of the pattern in our tool brought out specific empirical evidence of its benefits, which we discuss in this paper.