Reviewerly/exHarmony huggingface dataset:
Hashing and Hashed Message Authentication Codes

Now that you know the importance of random numbers in cryptography and how to generate them, let’s look at one of the pillars of cryptography. In Chapter 2 , I mentioned the four pillars of modern cryptography: cryptography, integrity, authentication, and non-repudiation. In this chapter, we explore integrity and authentication by looking at the various hashing and authenticated hashing operations available in .NET.
Binary Stochastic Filtering: a Solution for Supervised Feature Selection and Neural Network Shape Optimization.

Binary Stochastic Filtering (BSF), the algorithm for feature selection and neuron pruning is proposed in this work. Filtering layer stochastically passes or filters out features based on individual weights, which are tuned during neural network training process. By placing BSF after the neural network input, the filtering of input features is performed, i.e. feature selection. More then 5-fold dimensionality decrease was achieved in the experiments. Placing BSF layer in between hidden layers allows filtering of neuron outputs and could be used for neuron pruning. Up to 34-fold decrease in the number of weights in the network was reached, which corresponds to the significant increase of performance, that is especially important for mobile and embedded applications.
Transformación de la práctica pedagógica mediada por TIC a partir de la investigación acción

The objective of the research project developed by Paidopolis Group of the Industrial University of Santander, Colombia, is to determine how the use of ICT in the classroom transforms the educational practice of primary school teachers of the fifth grade from research action. It is therefore intended to contribute to the ongoing training of teachers, who face learning challenges for which they were not trained during their undergraduate studies, from the promotion of real learning communities, based on the theoretical foundation in disciplinary, didactic and pedagogical knowledge.
Progressive Label Distillation: Learning Input-Efficient Deep Neural Networks

Much of the focus in the area of knowledge distillation has been on distilling knowledge from a larger teacher network to a smaller student network. However, there has been little research on how the concept of distillation can be leveraged to distill the knowledge encapsulated in the training data itself into a reduced form. In this study, we explore the concept of progressive label distillation, where we leverage a series of teacher-student network pairs to progressively generate distilled training data for learning deep neural networks with greatly reduced input dimensions. To investigate the efficacy of the proposed progressive label distillation approach, we experimented with learning a deep limited vocabulary speech recognition network based on generated 500ms input utterances distilled progressively from 1000ms source training data, and demonstrated a significant increase in test accuracy of almost 78% compared to direct learning.
The Test Items Development of Debate Class for University Students based on Textlinguistics

The purpose of this study was to develop the test items for debate class for university students based on textlinguistics. In order to accomplish this research purpose, this study employed data collection of evaluating university students’ debate class according to test items based on textuality. Factor and reliability analyses were conducted in order to achieve the research goal. In factor analysis, all the test items could be approved valid, might have one communality, textuality. All the test items had a strong interdependency with one another. There was no need to exclude any item. The cohesion in textuality had a great impact on the total score of debate class. According to reliability analysis, the coefficient of Cronbach’s α is 0.988. The measure is highly reliable. Like the factor analysis, if any of the test items were added or excluded, the reliability would be unchanged. These results implied that the teaching method of debate class based on textlinguistics would be very valuable. Lastly, instructional implications for the classroom and suggestions for further study were also discussed.
Perception-Oriented Asymmetric Stereoscopic Images Coding

Significant research works have been devoted to asymmetric stereoscopic coding technique. Based on the perceptual theory of binocular suppression, in this method, one view of the stereo pair can be coded with a lower quality than the other, thus allowing to decrease bandwidth requirement for 3D visual content delivery without degrading subjective quality. However, with the condition that the quality gap between both images does not exceed a certain threshold. Some works proposed to fix this threshold by subjective experiments. Nevertheless, using a fixed threshold does not allow adaptation to the stereo image contentand presents dependency to the experimental design. In this paper, we propose a novel method that selects automatically the maximum tolerable quality gap between views for different stereo image pairs. This has been achieved by modeling the relationship between the asymmetric quality and the inter-view distortion between corresponding pixels. Then, the derived relationship is combined with Binocular Just Noticeable Difference (BJND) model to control the quality threshold between the left and right views. Experimental results showed that the proposed coding method can achieve considerable bitrate saving up to16.32% and outperforms the widely used asymmetric coding approaches.
PENINGKATAN HASIL BELAJAR MATEMATIKA DENGAN MENGGUNAKAN METODE LATIHAN SIAP SISWA KELAS I C SD NEGERI 164 PEKANBARU

This research is classroom action research or classroom action research. The problem dealt with was from the low ability to complete thecount operations of grade I C SD Negeri 164 Pekanbaru. This learning problem is overcome by applying ready training methods. Theresearch aims to improve the ability of students to learn mathematics by applying ready training methods. Based on the results of researchand actions that by applying training methods are ready to be able to significantly improve learning outcomes in the IC SD Negeri 164Pekanbaru. Before the action is taken, the classical average value is 45.25, or unfavorably; in the first cycle to 61.25 or enough; and thesecond cycle becomes 81.50 or good. Before the action was carried out, students who completed their study at KKM 70 were only 13students or 32.50%; the first cycle becomes 22 students or 61.25%: and in the second cycle becomes 37 students or 92.50%. Based on theobservations of first-grade students of C SD Negeri 164 Pekanbaru, they seemed to understand the Ready Exercise Method and they couldwork independently in learning. The use of training methods is ready to successfully overcome the problem of the low mathematics learningoutcomes of students in the IC class of SD Negeri 164 Pekanbaru in the second cycle.
Calibration of geometric distortion based on a reference sheet in oblique laser interferometry

When the form deviation of curved surface is measured with oblique laser interferometry, the collected interferogram is badly distorted, and cannot be positioned on the measured surface directly. The simulated object image is employed as a bridge to solve the problem. However, the geometric distortion is ignored in the current measurement, and prevents a high accuracy of the registration result between measured surface and interferogram. This paper focuses on calibrating the geometric distortion to improve the registration accuracy. Firstly, the object image of a reference sheet is captured in the same position and measurement system with measured curved surface. Then, the object image is compared with its simulated object image to establish the geometric distortion model. Next, the geometric distortion is integrated with image foreshortening and anamorphic distortion to simulate a better object image. Finally, the experimental results are given to show the more accurate registration result, and verify the feasibility and effectiveness of the proposed method.
Collaborating E-book Collections: Lessons Learned &amp; Best Practices. A Report of the ALCTS Collection Management Section (CMS) Collection Development Librarians of Academic Libraries Interest Group Meeting. American Library Association Midwinter Meeting, Denver, February 2018

The Association for Library Collections & Technical Services (ALCTS) Collection Management Section (CMS) Collection Development Librarians of Academic Libraries Interest Group hosted a session enti...
On Knowledge of the Incompleteness Effect in Korean L1 Acquisition

Not only fully-completed events, accomplishment predicates with incremental themes in Korean can also describe partially-completed events, which is called the Incompleteness Effect (in short, the IE). Given the phenomenon, this experimental study examined two questions with 16 L1-Korean adult and 47 L1-Korean child speakers: (1) whether the validity of the IE can be empirically verified and (2) when children acquire the IE. The results showed that the 7-year-olds and adults could reliably link accomplishments to incomplete events but the 5- and 6-year-olds could not. They demonstrated full completion bias. The results suggest that children do not seem to be fully aware of the IE until as late as 6 years of age. This study argues that the pattern of data presented by the younger children is best explained by the Subset Principle, which states that children start out with the most restricted grammar compatible with input unless there is evidence to the contrary (i.e., full completion interpretations).
Traffic Safety Needs Is Pervasive in All Geographical Markets [Connected and Automated Vehicles]

Verifying the performance and correctness of a smart factory is difficult. This is because it is more complicated to verify the functionality of a distributed system than it is to verify a centralized system. Verifying a distributed heterogeneous system such as a factory is even worse. Now imagine that the distributed heterogeneous system has multiple stakeholders, with different, often competing business models. This is the actual situation for connected autonomous vehicles.
P.2.02 Bottom-up versus top-down attention during working memory encoding: A functional magnetic resonance imaging study

The media industry is continuously striving to improve image quality and to enhance the overall viewing experience, with higher frame rates, larger resolution, more vivid colours, and greater contrast. Currently there is significant emphasis on high dynamic range (HDR) and wide colour gamut (WCG) imaging. But we are still a long way from a fully functional HDR ecosystem, upon whose existence depends the realisation of all the possibilities that the HDR format could offer in terms of revenue and market growth for companies and in terms of improved user experience for viewers. Very recent reports from several international organisations and standardisation bodies concur in that there are a number of challenges that need to be addressed for a successful and complete adoption of HDR and WCG technology. This book is focused on solutions, based on vision science, for several of the issues just mentioned but especially for the problems of tone and gamut mapping. Our conclusions are that imaging techniques based on vision models are the ones that perform best for these problems and a number of other applications, but also that the performance of these methods is still far below what cinema professionals can achieve, and vision models are likewise lacking, most key problems in visual perception remain open.
Learning Taxonomies of Concepts and not Words using Contextualized Word Representations: A Position Paper.

Taxonomies are semantic hierarchies of concepts. One limitation of current taxonomy learning systems is that they define concepts as single words. This position paper argues that contextualized word representations, which recently achieved state-of-the-art results on many competitive NLP tasks, are a promising method to address this limitation. We outline a novel approach for taxonomy learning that (1) defines concepts as synsets, (2) learns density-based approximations of contextualized word representations, and (3) can measure similarity and hypernymy among them.
Blockchain Coupled Oracle Fusion

With stricter compliance and policy requirements, demanding customers, commercial competition, geographically spread businesses, and vulnerable cyber security, today's multifaceted business sees a lot more challenges than ever before. To overcome these challenges, there is a need to track and trace each aspect of the business process. Maintaining an extra protected automated system with transparency, security, reliability, and traceability has a big impact on the bottom line of business organizations. A technology called Blockchain provides a viable solution to the problems associated with the complex business process traceability. The blockchain architecture and design is based on distributed ledger technology (DLT). The Blockchain technology was first theorized to be used for a cryptocurrency called 'Bitcoin' for a peer-to-peer online payment in 2008 (in a white paper published by Satoshi Nakamoto). Off late, it has begun to witness success in finding applications in many other fields like banking, asset management, healthcare, travel, supply chain, logistics, identity management, etc. Here we discuss industrial applications of Blockchain in an Oracle Enterprise Resource Planning (ERP) Cloud environment. As per a study by Forbes, about 90% of the Fortune 500 companies work on some or the other Enterprise Resource Planning (ERP) platform (single or multi-ERP systems). By 2020, four out of every ten large organizations will have atleast 60% of their businesses on Cloud. Hence, Blockcha,in in a business scenario needs to have an integration with already existing ERP systems and furthermore with future cloud systems. Oracle Blockchain Cloud service offers easy application integration with plug-and-play adapters providing PaaS delivery.
Pointer Temperature and Humidity Meter Detection Based on Machine Vision

A new method is proposed to achieve the accurate segmentation and reading of different types of pointer temperature and humidity instruments. The Canny edge detection operator is applied to detect edges of the meter gray image, and the scale line distribution arc and scale lines is obtained according to distribution characteristics of the scale line. The pointer is fitted by concentric method within the radius of the arc. OCR identifies the scale value of the dial, and then determines the corresponding reading number of each scale line and dividing value by mapping relation. Three different types of meters are used to detect, and experiments results demonstrate that the proposed method can achieve accurate segmentation of pointer and scales, as well as accurately calculate the readings of instrument.
Validation loss for landmark detection.

We present a new loss function for the validation of image landmarks detected via Convolutional Neural Networks (CNN). The network learns to estimate how accurate its landmark estimation is. This loss function is applicable to all regression-based location estimations and allows the exclusion of unreliable landmarks from further processing. In addition, we formulate a novel batch balancing approach which weights the importance of samples based on their produced loss. This is done by computing a probability distribution mapping on an interval from which samples can be selected using a uniform random selection scheme. We conducted experiments on the 300W, AFLW, and WFLW facial landmark datasets. In the first experiments, the influence of our batch balancing approach is evaluated by comparing it against uniform sampling. In addition, we evaluated the impact of the validation loss on the landmark accuracy based on uniform sampling. The last experiments evaluate the correlation of the validation signal with the landmark accuracy. All experiments were performed for all three datasets.
Modal clustering asymptotics with applications to bandwidth selection.

Density-based clustering relies on the idea of linking groups to some specific features of the probability distribution underlying the data. The reference to a true, yet unknown, population structure allows to frame the clustering problem in a standard inferential setting, where the concept of ideal population clustering is defined as the partition induced by the true density function. The nonparametric formulation of this approach, known as modal clustering, draws a correspondence between the groups and the domains of attraction of the density modes. Operationally, a nonparametric density estimate is required and a proper selection of the amount of smoothing, governing the shape of the density and hence possibly the modal structure, is crucial to identify the final partition. In this work, we address the issue of density estimation for modal clustering from an asymptotic perspective. A natural and easy to interpret metric to measure the distance between density-based partitions is discussed, its asymptotic approximation explored, and employed to study the problem of bandwidth selection for nonparametric modal clustering.
Study of the Usability of an Adaptive Smart Home Interface for People with Alzheimer’s Disease

Research on information technologies has seen in the last 20 years a very fast growth showing an increase of attention to the development of solution able to satisfy people with different characteristics and needs. Today, one of the main research topics aims at the definition of technologies and tools to support elderly people independent living. In this context, the present paper proposes a new home automation system able to support people with the early-stage dementia in kitchen management. This system is managed through an adaptive interface, which guides the user in kitchen activities, provides information on the functioning of all devices in the kitchen specifically, allows to set and control all household appliances in a simple and intuitive way and gives information, adapting to the end-user’s capabilities and needs. Although this preliminary evaluation only included a small number of participants, the results showed that the introduction of a new smart home system can be useful to individuals suffering early-stage dementia to facilitate independent living and remain longer in their own homes.
Evaluation of Multidisciplinary Effects of Artificial Intelligence with Optimization Perspective

Artificial Intelligence has an important place in the scientific community as a result of its successful outputs in terms of different fields. In time, the field of Artificial Intelligence has been divided into many sub-fields because of increasing number of different solution approaches, methods, and techniques. Machine Learning has the most remarkable role with its functions to learn from samples from the environment. On the other hand, intelligent optimization done by inspiring from nature and swarms had its own unique scientific literature, with effective solutions provided for optimization problems from different fields. Because intelligent optimization can be applied in different fields effectively, this study aims to provide a general discussion on multidisciplinary effects of Artificial Intelligence by considering its optimization oriented solutions. The study briefly focuses on background of the intelligent optimization briefly and then gives application examples of intelligent optimization from a multidisciplinary perspective.
An Enhanced and Reliable Efficient Algorithm for Mining Authentication Utility Item Sets

High utility itemsets (HUIs) mining is a developing theme in information mining, which alludes to finding all itemsets having an utility gathering a client determined least utility edge min_util. In any case, setting min_util fittingly is a troublesome issue for clients. As a rule, finding a suitable least utility limit by experimentation is a dreary procedure for clients. On the off chance that min_util is set too low, such a large number of HUIs will be created, which may cause the mining procedure to be exceptionally wasteful. Then again, if min_util is set too high, all things considered, no HUIs will be found. we propose a strategy and framework for confirming messages is given. A message validation framework creates irregular string and that will be sent to the beneficiary's portable and message sent to the beneficiary by means of mail at the opposite side the collector got the message through the mail and that will be in encoded shape and the beneficiary will decode the message with key that is sent to his versatile. The message validation framework at that point decides if the recovered message coordinates the first message. In the event that the codes coordinate, the uprightness and validness of the message are confirmed.
Magnitude 6.0 earthquake hits western Iran

A magnitude 6.0 earthquake hit western Iran on Monday, state media reported.The centre of the quake
A study on Chinese people's everyday life information seeking: taking a comparison between web and Weibo

People's everyday life information seeking (ELIS) is essential in the information behaviour research. Prior literatures tend to focus on specific groups of countries and less is known about Chinese people's life information seeking behaviour. In this paper, an online survey was conducted to capture the background and behaviour data from users who regularly use web and Weibo, the most influential Chinese language information acquired channels. The 'friend-of-a-friend' recruitment technique received a total of 136 responses, 129 of which were valid and analysed to generate both descriptive and inferential statistics. Findings indicate that much information can be obtained through the two channels; however the information types were different across gender and education levels on the web and Weibo. Notably, variables including web use history, web frequency, and Weibo use frequency had significant relationships with values on the two channels.
Estimation of Parameters in Atmospheric Scattering Dehazing Model in Accordance with Visual Characteristics

In view of the problem that the restoration effect of the daytime defogging algorithm is not ideal, especially the over-enhancement and color distortion in the sky and its nearby. A new parameter estimation method for atmospheric scattering model is proposed. Firstly, the sky and non-sky areas are segmented. Then, estimating the atmospheric light at the junction, and then corresponding restrictions on the value of transmittance according to the change of the depth of field. Finally, the transmittance is optimized by context-based regularization, so that the final image after dehazing is more in line with the visual characteristics. Through the subjective comparison and analysis with the existing mainstream algorithms, the dehazing effect of the proposed method has the advantages of low noise and high color recovery, especially in the sky. The restoration with the non-sky junction is the best, enriching the details that other algorithms have not restored and the colors are true and natural.
Machine Learning Based Probing of Constraint Programming [using Dlib-Gecode] in the Context of Understanding Protein Folding Mechanisms – An Insight into Designing a Novel & Simple C++ Informatics Framework.

Machine Learning Based Probing of Constraint Programming [using Dlib-Gecode] in the Context of Understanding Protein Folding Mechanisms – An Insight into Designing a Novel & Simple C++ Informatics Framework.