Reviewerly/exHarmony huggingface dataset:
Cell Segmentation Using Multiple Instance Learning Based Support Vector Machines

In this study, in order to perform cell segmentation on histopathological images, multiple instance learning (MIL) paradigm is applied. In this context, support vector machines adapted to multiple instance learning problems are utilized in the classification modelling. Then, during the test phase, the test images are scanned and classified at pixel level separately and presegmentation images are obtained. In the post processing step, the Markov random fields (MRA) method is applied to improve the pre-segmentation results. In the conclusion, the classification performances of multiple instance based support vector machines and the conventional support vector machines are given comparatively.
Neural Machine Translation System using a Content-equivalently Translated Parallel Corpus for the Newswire Translation Tasks at WAT 2019

This paper describes NHK and NHK Engineering System (NHK-ES)'s submission to the newswire translation tasks of WAT 2019 in both directions of Japanese→English and English→Japanese.In addition to the JIJI Corpus that was officially provided by the task organizer, we developed a corpus of 0.22M sentence pairs by manually, translating Japanese news sentences into English contentequivalently.The content-equivalent corpus was effective for improving translation quality, and our systems achieved the best human evaluation scores in the newswire translation tasks at WAT 2019.
Publisher's Note: Quantification of Einstein-Podolsky-Rosen steering for two-qubit states [Phys. Rev. A <b>93</b> , 020103(R) (2016)]

Received 29 August 2019DOI:https://doi.org/10.1103/PhysRevA.100.039901©2019 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasNonlocalityGeneral Physics
PENGGUNAAN APLIKASI QUIZIZZ BERBASIS SMARTPHONE DALAM MEMBANGUN KEMAMPUAN BERPIKIR KRITIS MAHASISWA

This study aims to analyze the improvement of students' critical thinking skills as a result of learning by using a smartphone-based quizizz application compared to those who get conventional learning. The method in this study used quasi-experimental research design randomized control group Pretest-Potstest design. The population of this study were 42 semester 6 economic education study program students while the sample from this study was class A as an experimental class with smartphone-based quizizz applications, class B as a control class using conventional learning models. Based on the results of data analysis, the average N-gain critical thinking ability was 0.62 for the experimental class and 0.53 for the control class. It can be concluded that there are significant differences between classes using smartphone-based quizizz applications compared to classes using conventional models in the learning process. Learning by using a smartphone-based quizizz application can further enhance students' critical thinking skills compared to conventional learning
Discursos del odio en la esfera pública: ¿hasta dónde debe llegar la legislación?

The centrality of digital platforms in political discourse has increased the plurality of the public sphere, but at the cost of increasing its incivility, and hate speech in particular. This article, which serves as an introduction to the special section on discourses of hate, places the origins of the current understanding of hate speech in the European postwar period. Given the evolution towards extremism in contemporary democracies, it is argued that the same post-national and cosmopolitan regime that emerged from the ashes of war, the European Union, should be at the forefront of a new legislation that would work as an international benchmark and as an alternative to the self-regulation of social network corporations
Constructicography at work: implementation and application of the German Constructicon

Abstract The paper introduces the implementation and application of the German Constructicon hosted at the University of Düsseldorf ( www.german-constructicon.de ). The ultimate goal of the German Constructicon Project (GCP) is not only to identify and describe oftentimes overlooked grammatical constructions, but also to offer comprehensive descriptions in a dictionary-like online repository. Based on constructicographic analyses of the construction family negating_connector, including geschweige denn (‘let alone’), the paper reports on the annotation categories and the computational work routine yielding construction entries.
Temporal Relationship Recognition of Chinese and Vietnamese Bilingual News Events Based on BLCATT

The temporal relationship identification of bilingual news events is essentially to find the temporal relationship between news events in different languages under the same topic. At present, the event temporal relationship recognition requires a lot of manpower to design a timeline-based template. The implicit semantic information in the sentence is difficult to obtain, and different language texts are difficult to represent in the same feature space. Therefore, it is difficult to obtain the temporal relationship of cross-language news events. To this end, this paper proposes a Bi-LSTM Cross Attention (BLCATT) model to identify the temporal relationship between two events. Firstly, the bilingual word vector is used to represent the Chinese and Vietnamese bilingual news texts. Secondly, using Bi-LSTM to capture the semantic information of the sentence. Using the attention mechanism combined with the trigger word to obtain the event semantic information of the enhanced trigger word information. The event semantic information including the temporal logic relationship is obtained by using the cross-attention mechanism combined with the trigger word and combines the two parts of the event semantic information as the event coding. Finally, the event coding and the event rule feature are combined to obtain the temporal relationship information, and then the event temporal relationship recognition is transformed into the multi-class problem. To solve. The experimental results show that the proposed model achieves good results.
Digital Image Processing for Detecting Industrial Machine Work Failure with Quantization Vector Learning Method

Todays, digital image processing is widely used in various fields to facilitate humans in doing work by analyzing videos or images for use in decision making in the industrial world. The use of industrial machine technology is one of the most important factors in efforts to facilitate human work, but an industrial machine is inseparable from work failure that can hinder the production process and cause harm to the industry. This study aims to detect a failure in industrial machinery by using video data of industrial machine movements recorded using a webcam camera. For the preprocessing stage, the image is resized then converted to grayscale imagery and segmented using the thresholding method, then morphological operations are performed with an opening operation, feature extraction is done by changing the binary image into a vector data that is used as input data in the classification process using the Learning Vector Quantization Neural Network Algorithm (LVQ NN) version 1. The results showed the results of the detection of machine working errors can be done well with an accuracy value of 94.24% in training and 92.38% in the testing phase.
Hyperspectral Image Restoration using Nonconvex Hybrid Regularization

Hyperspectral image (HSI) restoration is an essential pre-processing step in order to obtain more useful images for subsequent applications. However, traditional methods based on convex regularization or nonconvex spectral penalty alone are not able to fully exploit the spatial-spectral properties of the HSI datasets. In this paper, by utilizing the nonconvex spectral penalty and the nonconvex spatial penalty, we propose a novel nonconvex hybrid regularization (NHR) model, which can preserve the image features and remove the mixed noise, including Gaussian noise, stripes, deadlines, and etc. The corresponding optimization problem can be efficiently solved using an iterative algorithm based on the Augmented Lagrangian Multipliers (ALM) method. Experimental results on both simulated and real HSI images prove that the proposed NHR method significantly improves the image quality.
FLO: Fast and Lightweight Hyperparameter Optimization for AutoML.

Integrating ML models in software is of growing interest. Building accurate models requires right choice of hyperparameters for training procedures (learners), when the training dataset is given. AutoML tools provide APIs to automate the choice, which usually involve many trials of different hyperparameters for a given training dataset. Since training and evaluation of complex models can be time and resource consuming, existing AutoML solutions require long time or large resource to produce accurate models for large scale training data. That prevents AutoML to be embedded in a software which needs to repeatedly tune hyperparameters and produce models to be consumed by other components, such as large-scale data systems. We present a fast and lightweight hyperparameter optimization method FLO and use it to build an efficient AutoML solution. Our method optimizes for minimal evaluation cost instead of number of iterations to find accurate models. Our main idea is to leverage a holistic consideration of the relations among model complexity, evaluation cost and accuracy. FLO has a strong anytime performance and significantly outperforms Bayesian Optimization and random search for hyperparameter tuning on a large open source AutoML Benchmark. Our AutoML solution also outperforms top-ranked AutoML libraries in a majority of the tasks on this benchmark.
Chat Box: Two-way Schemed Communication Device for Speech and Hearing Impairment

Distress caused by a verbal barrier which hinders the subjects from being able to communicate freely and easily to sign language incapable individuals and vice-versa is the focal point of concern this project aims to resolve. This communication device consists of two major parts: (1) a surface consists of two Bare Conductive Touch Boards preloaded with the twelve phrases and words with its corresponding display of hand sign images converted to its English translation and (2) two TFT Touch Shields mounted in Arduino Mega 2560 Rev3 respectively, preloaded with the standard English alphabet and American Sign Language symbols. The impaired subjects have two options either to tap the desired electrode pads on the Touch Board or to communicate using the TFT Touch Shield like a multi-touch texting option. On the other hand, the normal user communicates only via TFT Touch Shield. Chat Box is a communication device that bridges the language gap of a deaf, mute and deaf-mute, sign language capable individuals to those of normal, non-sign language people. The Chat Box indeed is an innovative piece of technology that has a potential of altering the inconvenient manner of conversing with the speech and hearing impaired community. The third-party programs and software proved the authenticity of the device by verifying its accuracy, consistency, speed (timing and delay) and responsiveness in terms of transmitting data from one LCD shield to the other, as well as the rate of the instantaneous audio produced once the electrode pads were tapped.
Notice of Retraction: Interpretable Facial Semantic Extractor Based on Axiomatic Fuzzy Set

Retracted.
Self-Supervised Unconstrained Illumination Invariant Representation.

We propose a new and completely data-driven approach for generating a photo-consistent image transform. We show that simple classical algorithms which operate in the transform domain become extremely resilient to illumination changes. This considerably improves matching accuracy, outperforming the use of state-of-the-art invariant representations as well as new matching methods based on deep features. The transform is obtained by training a neural network with a specialized triplet loss, designed to emphasize actual scene changes while attenuating illumination changes. The transform yields an illumination invariant representation, structured as an image map, which is highly flexible and can be easily used for various tasks. We point out that the utility of our method is not restricted to handling illumination invariance, and that it may be applied for generating representations which are invariant to additional types of nuisance, undesired, image variants
La Virtual Light Machine : un programme pionnier de visualisation musicale, son code et sa communauté

Foreshadowing in purpose and execution the music visualizers that were widely distributed with software media players during the early 2000s, Jeff Minter’s Virtual Light Machine (VLM) was distributed in the firmware of the commercially unsuccessful Atari Jaguar CD games console, which was released in 1995. The VLM was designed to play an audio CD and generate real-time animations in more-or-less tightly coupled synchrony with music. The following year, Minter published “Yak’s Quick Intro to VLM Hacking”, an online guide describing how to customize the visualizer’s 81 graphical presets that revealed a hidden menu in the software. Minter’s software work, not widely known outside of the community of video-game historians and enthusiasts, deserves inclusion in a broader history of consumer music visualization technology. I draw on born-digital primary sources—including newsgroup posts, web pages, and the original code for the VLM itself—to understand the extent to which the practices explicitly and implicitly endorsed by Minter are congruent with our contemporary understanding of hacking.
Challenges for Multimedia Research in E-Sports Using Counter-Strike

That video and computer games have reached the masses is a well-known fact. However, game streaming and, therefore, watching other people play videogames has also outgrown its humble beginnings by far. Game streams, be it live or recorded, are viewed by millions. Many of the streams are broadcasting competitive multiplayer games. This is called e-sports and it is very similar to sports broadcasting. E-sports is organized in leagues and tournaments in which players can compete in controlled environments and viewers can experience the matches, discuss and criticize just like in physical sports. In this paper, we look into the challenges for computer science in general and multimedia research in particular. The multimedia research community has done a lot of work on video streaming, broadcasting and analyzing the audience, but has missed the opportunity to investigate e-sports in detail. We focus on one particular game we deem representative for e-sports, Counter-Strike: Global Offensive, and investigate how the audience consumes game streams from competitive tournaments.
Effect of Augmented Reality on Astronomical Observation Instruction

Knowledge about astronomy is an important part of natural science; however, students taking the course of observing the outer space and stars cannot efficiently engage in the activity that poor learning outcome results from the limits of time, place, weather, and flat teaching materials. Although simulation software or paper-based planispheres have been applied to support astronomy learning, students cannot clearly understand the three-dimensional nature of astronomical concepts. Recently, due to the rapid advancement of technology, augmented reality (AR) is applied more and more to education. With the support of AR, classrooms are provided with real-and-virtual-combined learning equipment for students to reach the goal of visualizing complicated space relationships and abstract conception. It seems to bring a new opportunity for astronomy learning. However, although there have been studies on applications of augmented reality, few of them are concerned about empirical investigation on astronomical observation. This study therefore aims to establish an augmented-reality learning system on mobile devices and compare the differences among learning aids of a paper-based planisphere, computer simulation, and augmented reality on cognitive learning achievement and stargazing skill on astronomical instruction. Three classes of elementary students, in total, 87 students, participated in this study. One class used augmented reality on mobile devices to conduct astronomical observation, another class used simulation, and the other class used paper-based planispheres. The results indicate that augmented reality was effective in improving astronomy knowledge and stargazing skill.
Understanding the Impact of Empowerment on Employees Innovation Performance: Evidence from the Jordanian Hotel Industry

This research aimed to measure the impact of empowerment on employees' innovation performance at five-star hotels in Jordan.The research applied the two main perspectives of empowerment (i.e., structural empowerment and psychological empowerment).A 35-item questionnaire, measuring empowerment and innovation performance, was distributed to 400 employees working in 12 five-star hotels in Jordan with a 63.5 response rate.Principle component analysis was utilized to determine the factor structure for both empowerment and innovation performance and regression analysis to determine the impact of empowerment on innovation performance.The results reveal that both forms of empowerment have a positive and significant impact on innovation performance.However, structural empowerment has a stronger impact on innovation performance than psychological empowerment.The results also show that integrating the two perspectives of empowerment together has clearly a higher level of impact on innovation performance than structural and psychological empowerment does when both taken separately.This research provides new insights into the existing literature, implications and directions for future research.
Study on S-box Properties of Convolution Coder

The substitution box (S-box) is an essential component in cryptography. The desirable cryptographic properties of the S-box are bijective, non-linearity, strict avalanche, bit independence, and resiliency. The analyses of these properties determine the quality of S-box. This paper provides mathematical analysis of cryptographic properties of the convolution coder. Simulation results show that it satisfies most of the properties and robustness increases with the number of iteration.
Ray: A Distributed Execution Engine for the Machine Learning Ecosystem

Author(s): Moritz, Philipp C | Advisor(s): Jordan, Michael I; Stoica, Ion | Abstract: In recent years, growing data volumes and more sophisticated computational procedures have greatly increased the demand for computational power. Machine learning and artificial intelligence applications, for example, are notorious for their computational requirements. At the same time, Moores law is ending and processor speeds are stalling. As a result, distributed computing has become ubiquitous. While the cloud makes distributed hardware infrastructure widely accessible and therefore offers the potential of horizontal scale, developing these distributed algorithms and applications remains surprisingly hard. This is due to the inherent complexity of concurrent algorithms, the engineering challenges that arise when communicating between many machines, the requirements like fault tolerance and straggler mitigation that arise at large scale and the lack of a general-purpose distributed execution engine that can support a wide variety of applications.In this thesis, we study the requirements for a general-purpose distributed computation model and present a solution that is easy to use yet expressive and resilient to faults. At its core our model takes familiar concepts from serial programming, namely functions and classes, and generalizes them to the distributed world, therefore unifying stateless and stateful distributed computation. This model not only supports many machine learning workloads like training or serving, but is also a good t for cross-cutting machine learning applications like reinforcement learning and data processing applications like streaming or graph processing. We implement this computational model as an open-source system called Ray, which matches or exceeds the performance of specialized systems in many application domains, while also offering horizontally scalability and strong fault tolerance properties.
Efficiently Realizing Interval Sequences

We consider the problem of realizable interval sequences. An interval sequence is comprised of $n$ integer intervals $[a_i,b_i]$ such that $0\le a_i\leq b_i \le n-1$ and is said to be graphic/realizable if there exists a graph with degree sequence, say, $D=(d_1,\ldots,d_n),$ satisfying the condition $a_i\leq d_i\leq b_i$ for each $i\in[1,n]$. There is a characterization (also implying an $O(n)$ verifying algorithm) known for realizability of interval sequences, which is a generalization of the Erdös--Gallai characterization for graphic sequences. However, given any realizable interval sequence, there is no known algorithm for computing a corresponding graphic certificate in $o(n^2)$ time. In this paper, we provide an $O(n \log n)$ time algorithm for computing a graphic sequence for any realizable interval sequence. In addition, when the interval sequence is nonrealizable, we show how to find a graphic sequence having minimum deviation with respect to the given interval sequence in the same time. Finally, we consider variants of the problem, such as computing the most-regular graphic sequence and computing a minimum extension of a length $p$ nongraphic sequence to a graphic one.
An Improved Stochastic Model for Cybersecurity Risk Assessment

Most of the existing solutions in cybersecurity analysis has been centered on identifying threats and vulnerabilities, and also providing suitable defense mechanisms to improve the robustness of the cyberspace network. These solutions lack effective capabilities to countermeasure the effect of risks and perform long-term prediction. In this paper, an improved risk assessment model for cyberspace security that will effectively predict and mitigate the consequences of risk was developed. Real-time vulnerabilities of a selected network were scanned and analysed and the ease of vulnerability exploitability was assessed. A Risk Assessment Model was formulated using the synergy of Absorbing Markov Chain and Markov Reward Model. The model was utilized to analyse cybersecurity state of the selected network. The proposed model was simulated using R- Statistical Package, and its performance was evaluated by benchmarking with an existing model, using Reliability and Availability as metrics. The result showed that the proposed model has higher reliability and availability over the existing model. This implied that there is a significant improvement in the assessment of security situations in a cyberspace network.
Suitable Task Allocation in Intelligent Systems for Assistive Environments

The growing need of technological assistance to provide support to people with special needs demands for systems more and more efficient and with better performances. With this aim, this work tries to advance in a multirobot platform that allows the coordinated control of different agents and other elements in the environment to achieve an autonomous behavior based on the user’s needs or will. Therefore, this environment is structured according to the potentiality of each agent and elements of this environment and of the dynamic context, to generate the adequate actuation plans and the coordination of their execution.