Reviewerly/exHarmony huggingface dataset:
Guiding the Emergence of Excellent Large Digital Systems

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In their early years many major technologies have failed to create the huge productivity gains they would eventually deliver</i> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">. Today, many emergent and large-scale digital systems display weaknesses parallel to immature systems of the past. System builders need to learn from campaigns that created productivity-driving systems in the past and in recent years and build highly effective large systems for our future. Insights into the opportunities are presented in this article.</i>
ECC Atomic Block against Strong Side-Channel Attacks Using Binary Curves

There are many side-channel attacks against elliptic curve cryptography (ECC), such as Simple Power Analysis, Horizontal Collision Correlation Analysis, Improving the Big-Mac Attack and Differential Power Analysis. The countermeasures against those attacks are important research tasks in cryptography. Those side-channel attacks are executed by observing power consumption when the device implemented in cryptosystem performs cryptographic processing. From the viewpoint of the power consumption, we propose two atomic blocks which are the countermeasures against the above attacks for ECC over finite fields of characteristic two. In this paper, considering the possibility of stronger side-channel attacks appearing in the future, we assume a threat model and our atomic blocks are constructed to be safety for strong side-channel attackers with more powerful abilities.
Suggestions for the Improvement of Korean Grammar Teaching - Focusing on the Korean Language Teacher Training Degree Program -

The purpose of this study is to prepare a plan for improving the Korean grammar teaching subject of the Korean language teacher training degree program. The problem with the current curriculum is that the preparatory teachers could not complete the degree course because they did not learn the necessary contents adequately or did not do all the required subjects because of the completion time, which did not reflect the characteristics of the curriculum. In addition, because the guidelines for teaching practical education are given in the same way, the transition from theoretical grammar to educational grammar, which must be dealt with in the grammar pedagogy course, is only dealt with briefly and not covered fully. This is because The problem was that the curriculum operation is difficult the preparatory teachers to have expertise. This study proposes that, after discussing the expertise of the grammar teacher, the course be divided into “Korean grammar teaching 1,” which deals with the contents of educational grammar, and “Korean grammar teaching 2,”’ which covers the teaching methodology. At the same time, the contents of grammar teaching should be assigned to the Korean grammar teaching subjects, referring to the lecture topics of the current degree program.(Korea University)
Identity, Gender, and Age Recognition Convergence System for Robot Environments

This paper proposes a new dentity, gender, and age recognition convergence system for robot environments. In a robot environment, it is difficult to apply deep learning based methods because of various limitations. To overcome the limitations, we propose a shallow deep-learning fusion model that can calculate identity, gender, and age at once, and a technique for improving recognition performance. Using convergence network, we can obtain three pieces of information from a single input through a single operation. In addition, we propose a 2D / 3D augmentation method to generate virtual additional datasets for learning data. The proposed method has a smaller model size and faster computation time than existing methods and uses a very small number of parameters. Through the proposed method, we finally achieved 99.35%, 90.0%, and 60.9% / 94.5% of performance in identity recognition, gender recognition, and age recognition. In all experiments, we did not exceed the state-of-the-art results, but compared to other studies, we obtained performance similar to the previous study using only less than 10% parameters. In some experiments, we also achieved state-of-the-art result.
A Security Steganography Scheme Based On HDR Image

It is widely recognized that the image format is crucial to steganography for that each individual format has its unique properities. Nowadays, the most famous approach of digital image steganography is to combine a well-defined distortion function with efficient practical codes such as STC. And numerous researches are concentrated on spatial domain and jpeg domain. However, whether in spatial domain or jpeg domain, high payload (e.g., 0.5 bit per pixel) is not secure enough. In this paper, we propose a novel adaptive steganography scheme based on 32-bit HDR (High dynamic range) format and Norm IEEE 754. Experiments show that the steganographic method can achieve satisfactory security under payload from 0.3bpp to 0.5bpp.
Model &amp; Feature Agnostic Eye-in-Hand Visual Servoing using Deep Reinforcement Learning with Prioritized Experience Replay

This paper presents a feature agnostic and model-free visual servoing (VS) technique using deep reinforcement learning (DRL) which exploits two new architectures of experience replay buffer in deep deterministic policy gradient (DDPG). The proposed architectures are significantly fast and converge in a few numbers of steps. We use the proposed method to learn an end-to-end VS with eye-in-hand configuration. In traditional DDPG, the experience replay memory is randomly sampled for training the actor-critic network. This results in a loss of useful experiences when the buffer contains very few successful examples. We solve this problem by proposing two new replay buffer architectures: (a) min-heap DDPG (mH-DDPG) and (b) dual replay buffer DDPG (dR-DDPG). The former uses a min-heap data structure to implement the replay buffer whereas the latter uses two buffers to separate “good” examples from the “bad” examples. The training data for the actor-critic network is created as a weighted combination of the two buffers. The proposed algorithms are validated in simulation with the UR5 robotic manipulator model. It is observed that as the number of good experiences increases in the training data, the convergence time decreases. We find 27.25% and 43.25% improvements in the rate of convergence respectively by mH-DDPG and dR-DDPG over state-of-the-art DDPG.
Liver Metastasis Segmentation by Anisotropic Diffusion Filtering, Chan-Vese Algorithm, and Kmeans Clustering

Computed Tomography (CT) imaging is used extensively for many purposes such as diagnosing liver diseases and measuring the volume for surgery. In order to perform computer aided diagnosis for the liver and lesion, segmentation procedures have to be performed as a fundamental primary step in order to derive quantitative biomarkers for computer-aided decision support systems. Segmentation of the lesion is not an easy automatic task because the surrounding normal tissues around the liver have close intensity values to the liver which can lead to over segmentation. In order to solve that problem and segment the lesions accurately, the liver has to be segmented first. This paper demonstrates a method to segment liver lesions automatically in CT abdomen images by using Anisotropic Diffusion filtering, Chan-Vese algorithm, K-means clustering, and morphological binary operations.
On the origins of a 13-second segment of <i>Primordial Dance</i>: a brief Karl Sims interview with commentary

In this brief but highly focused interview, pioneer computer animation artist Karl Sims addresses the extent to which a particularly striking segment of his ground-breaking 1991 Primordial Dance has been influenced by early twentieth-century abstract art.
Continuation-Based Metric Semantics for Concurrency

By using the mathematical methodology of metric semantics and continuation semantics for concurrency (CSC) we design semantic models for a concurrent language extended with multiparty synchronization based on Hoare's CSP. We present a new kind of domain for CSC, where the structure of continuations is expressed by using functions which map computations to computations. We study the abstractness of CSC, by using a new optimality criterion specific to continuation semantics, that we call k weak abstractness - a weaker version of Milner's full abstractness criterion.
Discovering What You Cared by Intelligent Recommender System

Recently, online shopping has become very prominent. Furthermore, most e-commerce platforms use recommender systems to suggest products to the user for potential purchase to help enhance the user experience. Existing recommender models determine the correlation between user profiles and product information to make product recommendations. Although current recommender systems can accurately suggest products, there is a lack of transparency in terms of how the recommendations are made. As a result, users may not trust the suggestions made by what could be perceived as a black box. To address this problem, we have designed a deep recommender system that discovers user preferences and provides the reasons behind the recommendations it makes by examining clues such as user reviews and item descriptions. We adopt the attention mechanism to enhance text analysis capability, which not only enables accurate recommendations but also shows the relationship between products and user profiles. Furthermore, we conduct a case study in which we utilize feature weights to present the reasons to the user by observing attention maps. The results of our experiment show that our model can consistently provide accurate recommendations and justifications to the user. Moreover, our model can handle new items and users, that is, data that is not part of the initial training dataset. Our recommender system is a novel solution that leverages the advantages of content-based and collaborative filtering.
Evaluation System of Human Settlement Speciality Learning Roadmap Based on Machine Learning

The application of machine learning is becoming more widespread, especially in the fields of consumption and medical treatment, while its application in field of education is relatively rare. In this paper, based on the diversion requirements of large-scale speciality platform of the human settlement discipline system, artificial method is used to establish evaluation feature items and set up the evaluation system model. Meanwhile, under the condition of the small sample set, the machine learning method is used to train and adjust parameters to form the first edition of speciality potential assessment system combining with the artificial method, which would lay the foundation for the introduction of more courses and samples to upgrade the system in the future, in the hope to construct more accurate and in-depth potential evaluation to guide students with a more effective method.
Video Summarization based on Sparse Subspace Clustering with Automatically Estimated Number of Clusters

Advancements in technology resulted in a sharp growth in the number of digital cameras at people's disposal all across the world. Consequently, the huge storage space consumed by the videos from these devices on video repositories make the job of video processing and analysis to be time-consuming. Furthermore, this also slows down the video browsing and retrieval. Video summarization plays a very crucial role in solving these issues. Despite the number of video summarization approaches proposed up to the present time, the goal is to take a long video and generate a video summary in form of a short video skim without losing the meaning or the message transmitted by the original lengthy video. This is done by selecting the important frames called key-frames. The approach proposed by this work performs automatic summarization of digital videos based on detected objects' deep features. To this end, we apply sparse subspace clustering with an automatically estimated number of clusters to the objects' deep features. The summary generated from our scheme will store the meta-data for each short video inferred from the clustering results. In this paper, we also suggest a new video dataset for video summarization. We evaluate the performance of our work using the TVSum dataset and our video summarization dataset.
Random rules and the ancient history of simulation

Modern approaches to simulation, involving Monte Carlo methods and randomized procedures of decision-making, are usually dated back to the mid-20-th century and the arrival of the computer era. Deeper history goes back to the 19-th and even 18-th centuries and involves such devices as Galton’s boar d and Buffon’s needle. However, one can argue that long before the invention of computers, older devices such as dice and their predecessors have been effectively used for games and divination. The idea of this paper is to review the use of ancient randomizing devices to trace the history of simulation and random rules of decision-making. Special attention will be paid to some contemporary cultures, which have preserved some unique elements of their ancient history: native cultures of the Americas, the Celtic civilizations of Ireland and Scotland, and the indigenous peoples of Northern and Central Asia (Altai and Siberia).
Sensory Media Association through Reciprocating Training

Machine learning achieved great progress in recent years. However, state-of-the-art machine learning systems are still far behind biological learning systems on learning directly from sensors without offline labeling. This paper proposes an approach for automating machine learning from multi-modal sensors. In this learning setup, the system has no access to any human labeling tool which is not available to a biological learning system such as a dog or a newborn baby. We tested the learning proposal with audiovisual data. The testing system contains two deep autoencoders, one for learning speech representations and another for learning image representations. Two deep networks are trained to bridge the latent spaces of two autoencoders, yielding representation mappings for both speech-to-image and image-to-speech. To improve feature clustering in both latent spaces, the system alternately uses one modality to guide the learning of another modality. Different from traditional technology that uses a fixed modality for supervision (e.g. using text labels for image classification), the proposed approach facilitates a machine to learn from sensory inputs of two or more modalities through alternating guidance among these modalities. We evaluate the proposed model with MNIST digit images and corresponding digit speeches in the Google Command Digit Dataset (GCDD) and got very promising results.
A New Code for Encoding All Monotone Sources With a Fixed Large Alphabet Size

The problem of designing a fixed code for encoding all monotone sources with a large alphabet size n is studied. It is proved that if the code-lengths of a code sequence are of order O(log n), then the redundancy for almost all monotone sources with n symbols is very close to that for a specific distribution, so-called average distribution. Noting this point, for any large alphabet size n, a new code with a very simple structure is proposed whose redundancy is close to that of the Huffman code for almost all monotone sources with alphabet size n.
‘Democrazy’ designing for democracy in Eastern Europe

For design to attend to democratic endeavours it is not enough to rest on the claim that design is implicitly political, but to understand how democratisation — often in the name of political modernisation — has designed different social realities. Focusing on the ‘how to’ of infrastructuring for democracy has advanced a designerly politics-in-practice, and exploring political concepts in design experiments have made design more aware of the democratic conflict. Theoretical work-in-progress, this paper asks whether the concepts internalised within design literature are valid enough to think about infrastructuring for democracy in the context of Eastern Europe. We depart from the theoretical and practical difference between design for politics and political design to 1) understand how each of these concepts enable a democracy to come in Eastern Europe’s Romania, and 2) what are the entry points for design research to understand the democratic experience. We explore this through a participatory intervention in Bucharest.
PROF 2.0: Improving a Methodology for Topic Map and Skill Tree Creation

This paper explores the Pedagogical Resource Organization Framework (PROF), which takes content from a current course, creates a Topic Map (TM), then transforms this TM into a Knowledge, Skills, and Abilities Tree (KSAT) by ingesting a schedule document to give direction. Transitioning from unorganized TMs to structured KSATs is the fundamental methodology educators follow to develop clear learning paths and education assessments for students [1]; this is a time consuming process. We initially evaluated PROF against a graduate level course and compared the resulting Topic Map to a manually created Topic Map for the same course to measure its effectiveness [2]. The results showed that in under one working hour PROF produced a TM containing over 36%exact matches and over 93% adjusted matches to the SME-Created TM. In this updated version of PROF, we re-evaluate this course as well as the Network Warfare Bridge Course (NWBC) hosted by the 39th Information Operations Squadron. Using a more exact whitelisting process to find specific lesson objectives, PROF increased the identification of topics for the initial course and identified over 51% of topics for the NWBC. PROF also produced a TM for each, with a more accurate TM for the initial course, as well as KSATs for both courses using nodes from the TM for educators to align learning objectives with course readings.
A Maneuvering Multi-target Tracking Algorithm Based on AGMM-CBMeMBer

Aiming at the problem that traditional tracking algorithms can not effectively track maneuvering multi-target in clutter environment, this paper proposes a new maneuvering multi-target tracking algorithm based on adaptive grid method and Cardinality Balanced Multi-Target Multi-Bernoulli (CBMEMBer) filtering algorithm. The adaptive adjustment of the model set is realized by introducing the adaptive grid method. Therefore, the proposed algorithm can adapt to the change of the moving state of the maneuvering target. The simulation results show that, compared with the fixed structure multi-model tracking algorithm, the proposed algorithm has better tracking performance for maneuvering multi-target, and its cost-effectiveness ratio is higher. It has a wide application prospect in the field of multi-target tracking.
Efficient Human Detection Algorithm using Color &amp; Depth information with Accurate Outer Boundary Matching

Foreground segmentation has a critical role in image processing and computer vision-based applications. Extracting accurate foreground pixels, especially in the boundary region is still an unsaturated area in image analysis. The proposed method explains a segmentation scheme to extract human area from an image or from video frames taken by RGB-Depth sensors like Microsoft Kinect camera. The pre-segmentation is achieved by using the depth information. Here in this algorithm, more focus is given for the enhancement of the pre-segmented image for accurate boundary matching of the segmented human factor. Since the depth analysis have limitations in detecting accurate border pixels, post processing steps are involved here for restoring the missing foreground portions and to avoid background pixels from the final segmented output. Initially hair area is restored using Chan-Vese active contour detection along with Viola-Jones face detection. For making the segmentation quality enough to use in multimedia applications like film editing, fine outer boundary estimation is performed using digital matting procedures.
Client-Side Cornucopia: Comparing the Built-In Application Architecture Models in the Web Browser

The programming capabilities of the Web can be viewed as an afterthought, designed originally by non-programmers for relatively simple scripting tasks. This has resulted in cornucopia of partially overlapping options for building applications. Depending on one’s viewpoint, a generic standards-compatible web browser supports three, four or five built-in application rendering and programming models. In this paper, we give an overview and comparison of these built-in client-side web application architectures in light of the established software engineering principles. We also reflect on our earlier work in this area, and provide an expanded discussion of the current situation. In conclusion, while the dominance of the base HTML/CSS/JS technologies cannot be ignored, we expect Web Components and WebGL to gain more popularity as the world moves towards increasingly complex web applications, including systems supporting virtual and augmented reality.
Expectation of Radar Returns from Ionosphere Using Decision Tree Technique

In this paper, the prediction of Decision Tree classification is assessed using two property attribute selection choice measures for the ionosphere dataset. Decision Tree utilizes isolate and vanquish system for the essential learning procedure. From the outcome investigation, we can reason that the execution of Decision Tree classification depends on the characteristic attribute selection choice measures. Decision Tree is valuable since development of choice tree classifiers does not require any area learning. The primary goal is to manufacture a proficient expectation demonstrate for ionosphere radar comes back with high exactness.
Conversion from Lossless to Gray Scale Image Using Color Space Conversion Module

Compression plays a vital role in better utilization of available memory space and bandwidth. Hence it is widely used for image archiving, image transmissions, multimedia applications, cloud-computing, E-commerce, etc. For effective presentation and utilization of digital image specific techniques are required to reduce the number of bits. There are two types of image namely Lossy and Lossless. Conversion from lossless to lossy images requires specialized and licensed software. A software prototype module has been developed using color space conversion module using OpenCV which converts lossless image to lossy image after resizing, further convert’s lossy image to gray scale image and vice-versa. The lossless images such as TIFF, BITMAP, and PNG are initially resized, converted to lossy image and further converted to Gray scale. On conversion to gray-scale it has been found that the number of bits has been considerably reduced. Performance analysis of software proto type is obtained by comparing the Decompressed image quality with respect to compression ratio, compression factor, PSNR (Power to Signal Noise Ratio) and MSE (Maximum Signal Error). The results of the developed color space module are compared and presented in the form of tables and snapshots.
Big Data and Automatic Detection of Topics: Social Network Texts

Abstract This paper proposes the analysis of the influence of terms that express feelings in the automatic detection of topics in social networks. This proposal uses an ontology-based methodology which incorporates the ability to identify and eliminate those terms that present a sentimental orientation in social network texts, which can negatively influence the detection of topics. To this end, two resources were used to analyze feelings in order to detect these terms. The proposed system was evaluated with real data sets from the Twitter and Facebook social networks in English and Spanish respectively, demonstrating in both cases the influence of sentimentally oriented terms in the detection of topics in social network texts.