Reviewerly/exHarmony huggingface dataset:
Brief Note: Asynchronous Verifiable Secret Sharing with Optimal Resilience and Linear Amortized Overhead

In this work we present hbAVSS, the Honey Badger of Asynchronous Verifiable Secret Sharing (AVSS) protocols - an AVSS protocol that guarantees linear amortized communication overhead even in the worst case. The best prior work can achieve linear overhead only at a suboptimal resilience level (t < n/4) or by relying on optimism (falling back to quadratic overhead in case of network asynchrony or Byzantine faults). Our protocol therefore closes this gap, showing that linear communication overhead is possible without these compromises. The main idea behind our protocol is what we call the encrypt-and-disperse paradigm: by first applying ordinary public key encryption to the secret shares, we can make use of highly efficient (but not confidentiality preserving) information dispersal primitives. We prove our protocol is secure under a static computationally bounded Byzantine adversary model.
Machine Learning of Time Series Using Time-delay Embedding and Precision Annealing

Tasking machine learning to predict segments of a time series requires estimating the parameters of a ML model with input/output pairs from the time series. Using the equivalence between statistical data assimilation and supervised machine learning, we revisit this task. The training method for the machine utilizes a precision annealing approach to identifying the global minimum of the action (-log[P]). In this way we are able to identify the number of training pairs required to produce good generalizations (predictions) for the time series. We proceed from a scalar time series $s(t_n); t_n = t_0 + n \Delta t$ and using methods of nonlinear time series analysis show how to produce a $D_E > 1$ dimensional time delay embedding space in which the time series has no false neighbors as does the observed $s(t_n)$ time series. In that $D_E$-dimensional space we explore the use of feed forward multi-layer perceptrons as network models operating on $D_E$-dimensional input and producing $D_E$-dimensional outputs.
PEMBUATAN SISTEM INFORMASI ADMINISTRASI ORGANISASI MAHASISWA UNIVERSITAS SURABAYA

University of Surabaya (Ubaya) is one of the private universities in Surabaya, East Java. In general, any university that is both private and state universities have student organizations in accordance with the interests of each university. Similarly, the University of Surabaya. At present, the administrative activities that include recording and realization of the activities of the work program is still running it manually. Processes running manually is a time consuming, because there are several steps that must be done in a way to meet people. The duration of the administration process can impede the course of events. Therefore, making this system aims to help the administration process so that the process can be completed more quickly and does not inhibit the activity. Analysis of system requirements through interviews to the parties concerned and also made observations on the course of the administrative process. This system will streamline administrative processes and activities can save the file with the existing format seminimalis system design made possible so it does not make the user confused in using this system. The system is made by using CodeIgniter and phpMyAdmin. After going through the process of testing, the system created to help according to the needs of student organizations University of Surabaya. Final conclusion of this system can help reduce errors and help accelerate the process of administrative activities.
Revisiting a single-stage method for face detection

Although accurate, two-stage face detectors usually require more inference time than single-stage detectors do. This paper proposes a simple yet effective single-stage model for real-time face detection with a prominently high accuracy. We build our single-stage model on the top of the ResNet-101 backbone and analyze some problems with the baseline single-stage detector in order to design several strategies for reducing the false positive rate. The design leverages the context information from the deeper layers in order to increase recall rate while maintaining a low false positive rate. In addition, we reduce the detection time by an improved inference procedure for decoding outputs faster. The inference time of a VGA ($640{\times}480$) image was only approximately 26 ms with a Titan X GPU. The effectiveness of our proposed method was evaluated on several face detection benchmarks (Wider Face, AFW, Pascal Face, and FDDB). The experiments show that our method achieved competitive results on these popular datasets with a faster runtime than the current best two-stage practices.
Commentary on the Development of Perception and Cognition

Perceptual development is characterized by exploration and a search for invariants and affordances. As they are discovered, perception is differentiated and increasingly distinguished by embedded and superordinate structure. Perceptual learning does not require reinforcement as learning theories of the forties demanded; detecting the invariant, the order, the affordance in the event is itself reinforcing and the differentiation thus achieved is not lost. Differentiated perception” is not a “mode of perceiving” but a change in what is perceived. Order imposed by contingent relations in a task provides a structure that offers the opportunity for more economical processing, if it is picked up. Nested dimensions or potential “rule-giving” dimensions can reduce uncertainty; detection of such relations is of great utility.
User Interface Design

As we are heading toward an open-source digital experience platform, DXP’s provide collaborative user interface. This chapter provides DXP user interface (UI) concepts and shows how to develop intuitive and interactive UI designs. In this chapter we look at:
Adaptive Gradient Refinement for Adversarial Perturbation Generation

Deep Neural Networks have achieved remarkable success in computer vision, natural language processing, and audio tasks.
AN EFFICIENT TECHNIQUE FOR GLOBAL FACIAL RECOGNITION USING GABOR WAVELET AND SUPPORT VECTOR MACHINES IN 2D IMAGES

Ponte Academic JournalJan 2019, Volume 75, Issue 1 AN EFFICIENT TECHNIQUE FOR GLOBAL FACIAL RECOGNITION USING GABOR WAVELET AND SUPPORT VECTOR MACHINES IN 2D IMAGESAuthor(s): Jose Cadena ,Nora la Serna PalominoJ. Ponte - Jan 2019 - Volume 75 - Issue 1 doi: 10.21506/j.ponte.2019.1.5 Abstract:The article is presented in the context of 2D global facial recognition, using Gabor Wavelet's feature extraction algorithms, and facial recognition Support Vector Machines (SVM), the latter incorporating the kernel functions: linear, cubic and Gaussian, The objective is to observe the results of facial recognition in each case. An efficient technique is proposed that includes the mentioned algorithms for a database of 2D images. The technique has been processed in its training and testing phases, for the facial image databases FERET [1] and MUCT [2], and the models generated by the technique allowed to perform the tests, whose results achieved a facial recognition of individuals over 96%. Download full text:Check if you have access through your login credentials or your institution Username Password
Key Storage and Azure Key Vault

In previous chapters, we built a sophisticated encryption example that had the ability to use symmetric encryption to encrypt our data while protecting the symmetric encryption keys utilizing an RSA public and private key pair. While symmetric encryption is fast and efficient, moving the keys between multiple parties is very hard to achieve safely; this is why we use RSA to encrypt the symmetric key. The key is encrypted using the recipient’s public key (which can be known by anyone) and is then decrypted using the recipients private key (which only they know).
Reed-Solomon Codes over Fields of Characteristic Zero

We study Reed--Solomon codes over arbitrary fields, inspired by several recent papers dealing with Gabidulin codes over fields of characteristic zero. Over the field of rational numbers, we derive bounds on the coefficient growth during encoding and the bit complexity of decoding, which is polynomial in the code length and in the bit width of error and codeword values. The results can be generalized to arbitrary number fields.
Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces.

Modern implicit generative models such as generative adversarial networks (GANs) are generally known to suffer from instability and lack of interpretability as it is difficult to diagnose what aspects of the target distribution are missed by the generative model. In this work, we propose a theoretically grounded solution to these issues by augmenting the GAN's loss function with a kernel-based regularization term that magnifies local discrepancy between the distributions of generated and real samples. The proposed method relies on so-called witness points in the data space which are jointly trained with the generator and provide an interpretable indication of where the two distributions locally differ during the training procedure. In addition, the proposed algorithm is scaled to higher dimensions by learning the witness locations in a latent space of an autoencoder. We theoretically investigate the dynamics of the training procedure, prove that a desirable equilibrium point exists, and the dynamical system is locally stable around this equilibrium. Finally, we demonstrate different aspects of the proposed algorithm by numerical simulations of analytical solutions and empirical results for low and high-dimensional datasets.
The Current Status of Chinese International Students Who are Learning English and Required Support at a University in Korea

This study investigates Chinese international students’ English learning backgrounds in their homeland and the current state of their English studies at a university in Korea. In addition, the study seeks to explore these students’ difficulties and ascertains the required support for their English instruction. Fifty-two participants were involved in the study. Data collected from a survey and interviews were analyzed using SPSS. The age at which they started learning English, the duration of their English learning, and the quantity of weekly study time differed noticeably across students. Approximately, 20 percent of the students indicated they had difficulty comprehending their classes in English. Of the participants, 40 percent were satisfied with their English learning. However, 48 percent of the participants experienced difficulty in learning English through the courses required for their majors. The participants also discussed the advantages and disadvantages of learning English in Korea and the additional support they needed to effectively learn English in Korea. The results indicate that customized curricula and support for Chinese international students in Korea are necessary to produce a more effective English program.
Adaptive Image Dehazing via Improving Dark Channel Prior

The dark channel prior (DCP) technique is an effective method to enhance hazy images. Dark channel is an image with the same size as the hazy image which represents the haze severity in different places of the image. The DCP method suffers from two problems: it is incapable for removing haze from smooth regions, causing blocking effects on these areas; it cannot properly reduce a haze with a non-monotonic behavior. In this paper, an adaptive image dehazing method is proposed based on the DCP method to solve the problem of this method. In this method, to overcome the dark channel deficiency of the blocking effects, the dark channel is initially extracted. The hazy image is subsequently segmented into smooth and non-smooth regions. Regarding the smooth regions, the pixel values in the dark channel are reduced by dividing them with a rather great number. To solve the second problem, depending upon the haze severity, the haze removing technique is applied repeatedly until all the regions of the image are enhanced. Finally, the Gamma correction approach is used for contrast enhancement of the smooth regions. The performed subjective and objective comparison attest the superiority of the proposed method to the DCP one in removing the haze.
Traffic control using computer vision

Now a days traffic density on the streets increasing around the world tremendously . It causes several problems on the day to day life of people. As we know that it is the era of speed, so that nobody wants to wait for a long time at any cost. Everybody prefers to low traffic density streets. This proposed system introduced a vehicle density-based traffic control system to avoid above issues. This problem can be resolved by controlling the traffic density on the roads. This system introduces a new method to control vehicle density by controlling the traffic lights using Image processing. Vehicle density is measured using predefined classifiers available in image processing. If the measured density is above the normal density (threshold value) it passes an indication to the microcontroller which controls the projector and thereby we can give appropriate traffic signal to display.
Impact of High Performance Work Practices on Employee Engagement in Apparel Manufacturing and Retail Firms

This study explored the relation of high-performance work practices with employee engagement in apparel manufacturing and retail sector. The data were collected from 150 employees of apparel manufacturing and retail companies in Bangalore through a questionnaire survey. Data collected were run through factor analysis and regression. The results were in the expected direction and fulfilled the research aims of the current study. During factor analysis, five major factors emerged with high factor loadings from 0.40 to 0.80. High factor loadings were indicative of high engagement levels due to human resources (HR) practices followed. Companies can continue with HR practices, which makes them good places to work for employees and practice good leadership to motivate people to join the apparel and retail sector, which has been facing challenging times.
P.3.22 Can we assess cognition and functional capacity simultaneously? Validation of a virtual reality shopping task

Cyber-physical-social systems (CPSSs) contain integrated cyber parts, including computing, communication, and physical parts; it uses computations and communication embedded in and interacting with physical processes to add new capabilities to physical systems. While CPSSs opens new opportunities in various fields, it brings new challenges in security. By inventing anti-forensic tools, attackers may break the investigation system and make the analysis of attack scenarios difficult. In this chapter, we propose a scheme of forensics-awareness to support reliable image forensics investigations. The forensic scheme utilizes a Multichannel Convolutional Neural Network (MCNN) to learn hierarchical representations from the input images. Fake images can be identified using the well-trained MCNN. Most existing works aim at detecting a certain manipulation, which may usually lead to misled results, e.g. results with irrelevant features and/or classifiers. To overcome this limitation, we extract the periodicity property and filtering residual feature from the image blocks. The f-map is then generated by linear blending the periodic spectrum and the residual map, and is fed into the MCNN. The overall framework is capable of detecting different types of image manipulations, including cloning, removing, splicing and smoothing. Experimental results show that this scheme outperforms formerly existing ones.
PENERAPAN MODEL PEMBELAJARAN CONCEPT SENTENCE DENGAN MEDIA FLASH CARD UNTUK MENINGKATKAN KETERAMPILAN MENULIS PUISI PADA SISWA KELAS VI SD N 03 DELINGAN TAHUN AJARAN 2018/2019

Abstract: The objective of this research is to improve poetry-writing skill through the concept sentence learning model by using flash card media for sixth grade of state elementary school of delingan in academis year 2018/ 2019. The subject of the research were are a student and 25 students in Grade VI of the school in Academic 2018/2019. The data of the research were collected through in-depth interview, observation, test, and documentation. They were analyzed by using the interactive models of analysis which compreses four components, namely: data gathering, data reduction, data display, and conclusion drawing. The data were validated by using the data source and data collection triangulations technique. This research used the classroom activity research with two cycles. Each cycle consisted of four phases, namely: planning, implementation, observation, and reflection. Thus, the Concept Sentence Learning Model with Flash Card could improve poetrywritting skill of student in sixth grade of state elementary school of Delingan Year 2018/2019
Introduction to Neural Nets

Neural networks, or neural nets, are a popular way of implementing machine “intelligence.” The idea is that they behave like the neuron in a brain. In our taxonomy, neural nets fall into the category of true machine learning, as shown on the right.
Build Program Awareness Through Strategic Community Partnerships

Nonprofit Communications ReportVolume 17, Issue 3 p. 8-8 Creating Meaningful Partnerships Build Program Awareness Through Strategic Community Partnerships Megan Venzin, Megan VenzinSearch for more papers by this author Megan Venzin, Megan VenzinSearch for more papers by this author First published: 11 February 2019 https://doi.org/10.1002/npcr.31153Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume17, Issue3March 2019Pages 8-8 RelatedInformation
Effectiveness of Sentence Scramble Games to Improve Syntactic Abilities for a Student with Hearing Impairment

This article reviews the effectiveness of sentence scramble games to improve syntactic abilities of a student with hearing impairment who experienced difficulties while learning syntactic abilities even though the topic had been explained using flashcards. This research is an experimental study in the form of single subject research (SSR) with multiple baselines cross conditions design. The subject of the research is a student with hearing impairment. Data collection was performed through a written test and the data were in the form of student worksheets. The data were analyzed using a visual data analysis technique. Based on the results of the analysis of the intra-conditions and inter-condition of the classroom, computer room, and the boarding house, it was found that the syntactic abilities of the student with hearing impairment improved after an intervention using sentence scramble games was performed. The sentence scramble games have proven effective in improving the syntactic abilities of a student with hearing impairment.
Safely Storing Passwords

Now that we have covered hashing and authenticated hashing, the next place to explore is the protection and storage of passwords. Passwords are still the most common way to authenticate a user, but it is easy to put yourself in a situation where your system is not secure and is susceptible to attacks. In this chapter we will explore various different password storage techniques and discuss their benefits and consequences.
THE MODEL OF ADOPTION OF THE INFORMATION AND COMMUNICATION TECHNOLOGY FOR THE TEACHING PROCESS - LEARNING: CASE UNIVERSIDAD TECNICA DE COTOPAXI

Ponte Academic JournalJan 2019, Volume 75, Issue 1 THE MODEL OF ADOPTION OF THE INFORMATION AND COMMUNICATION TECHNOLOGY FOR THE TEACHING PROCESS - LEARNING: CASE UNIVERSIDAD TECNICA DE COTOPAXIAuthor(s): Juan Carlos Chancusig Chisag ,Javier Cruzado GamboaJ. Ponte - Jan 2019 - Volume 75 - Issue 1 doi: 10.21506/j.ponte.2019.1.6 Abstract:The present investigation framed in the development of an adoption Model of the TIC for the learning process education in the Technical University of Cotopaxi, specially to improve the university education, this owes for the constant advances in digital knowledge field and the educational technology, therefore the institutions must improve its academic organization and process them educational. In the last years the use of TIC has developed in the different education levels and specially in the universities that nowadays count with new technological resources much advanced in the particular case of the university in which the investigation is developing the study is, to improve the Educational Learning process implementing a new adoption model of TIC, with which it is possible to give solution to the educational process. There is realized a methodology of study based on the systematical Review of the literature of the models of the TIC citing as an example the first model that there appeared the Model TAM who was developed by Davis, 1986 and of the Web 2 and Web 3 and of the TIC in the Universities of the world. The investigation threw the results expected with the creation of the new model of adoption of TIC, with the use of collaborative hardware in a Learning environment (CLE) for making the constructs, alpha of cronbach, variance and interrelations and the use of statistical software as the minitab validated the raised hypotheses. Download full text:Check if you have access through your login credentials or your institution Username Password
Guaranteed Safe Reachability-based Trajectory Design for a High-Fidelity Model of an Autonomous Passenger Vehicle.

Trajectory planning is challenging for autonomous cars since they operate in unpredictable environments with limited sensor horizons. To incorporate new information as it is sensed, planning is done in a loop, with the next plan being computed as the previous plan is executed. The recent Reachability-based Trajectory Design (RTD) is a provably safe, real-time algorithm for trajectory planning. RTD consists of an offline component, where a Forward Reachable Set (FRS) is computed for the vehicle tracking parameterized trajectories; and an online part, where the FRS is used to map obstacles to constraints for trajectory optimization in a provably-safe way. In the literature, RTD has only been applied to small mobile robots. The contribution of this work is applying RTD to a passenger vehicle in CarSim, with a full powertrain model, chassis and tire dynamics. RTD produces safe trajectory plans with the vehicle traveling up to 15 m/s on a two-lane road, with randomly-placed obstacles only known to the vehicle when detected within its sensor horizon. RTD is compared with a Nonlinear Model-Predictive Control (NMPC) and a Rapidly-exploring Random Tree (RRT) approach. The experiment demonstrates RTD's ability to plan safe trajectories in real time, in contrast to the existing state-of-the-art approaches.
Making Convex Loss Functions Robust to Outliers using $e$-Exponentiated Transformation.

In this paper, we propose a novel {\em $e$-exponentiated} transformation, $0 \le e<1$, for loss functions. When the transformation is applied to a convex loss function, the transformed loss function become more robust to outliers. Using a novel generalization error bound, we have theoretically shown that the transformed loss function has a tighter bound for datasets corrupted by outliers. Our empirical observation shows that the accuracy obtained using the transformed loss function can be significantly better than the same obtained using the original loss function and comparable to that obtained by some other state of the art methods in the presence of label noise.