Reviewerly/exHarmony huggingface dataset:
Modification of R. Merkle’s Post-Quantum Algorithm

The report discusses the modification of RMSS algorithm (Ralf Merkle Signature Scheme) taking into account modern vulnerabilities. This report presents theoretical description of the post-quantum XMSS algorithm, differences between modification XMSS and classical RMSS, software implementation algorithm with increased cryptographic security and reduced size of the generated information with a small or insignificant decrease in performance, software tests and performance comparing using the Mann-Whitney U-test. This modification of the classical algorithm increase the number of keys available for signing, speed up the operation of creating signatures, and increase its reliability and security. It is using of random bit mask that allows replacing the family of collision-resistant hash-functions with family of hash-functions that are resistant to collisions of the second kind (second-preimage resistance). According to Mann-Whitney U-test results the differences between the classical implementation of the algorithm and the modified version are in the significance zone for both values of the probability of non-acceptance of the null hypothesis.
The equivariant complexity of multiplication in finite field extensions

We study the complexity of multiplication of two elements in a finite field extension given by their coordinates in a normal basis. We show how to control this complexity using the arithmetic and geometry of algebraic curves.
Conceptual complexity of neural networks

We propose a complexity measure of a neural network mapping function based on the order and diversity of the set of tangent spaces from different inputs. Treating each tangent space as a linear PAC concept we use an entropy-based measure of the bundle of concepts to estimate the conceptual capacity of the network. The theoretical maximal capacity of a ReLU network is equivalent to the number of its neurons. In practice, however, due to correlations between neuron activities within the network, the actual capacity can be remarkably small, even for very big networks. We formulate a new measure of conceptual complexity by normalising the capacity of the network by the degree of separation of concepts related to different classes. Empirical evaluations show that this new measure is correlated with the generalisation capabilities of the corresponding network. It captures the effective, as opposed to the theoretical, complexity of the network function. We also showcase some uses of the proposed measures for analysis and comparison of trained neural network models.
Video Mosaic of Arbitrary Viewing Direction Oriented on DAS

DAS (Distributed Apertures Sensors) system can acquire panoramic images from a distributed number of sensors, whose performance is mainly determined by the stitching algorithm. In DAS, the direction angle between sensors is large. Usually, rather than the whole panorama, vision with arbitrary viewing direction is required. In addition, low power consumption and high real-time stitching are expected in DAS. In this paper, we propose a stitching method for images with large capturing angle intervals, which can provide panorama with arbitrary observation direction. First, we project the captured image using cylinder model to deduce the feature difference between large capturing directions. Then the feature points are matched, projected back to original image, and screened out. Finally the distance parameter of stitching is estimated using the matched points, and the panorama of arbitrary observe direction is stitched. Additionally, we implemented the algorithm using FPGA, which is suitable for deployment in DAS systems with less computational resources. Experiment results suggest that the proposed algorithm improves the frame rate significantly (43.4fps) with no significant difference between the stitching effect of this paper and the comparison algorithm.
Analysis of Smart City Construction Based on 5G Data Technology

The advent of the 5G era has a very important influence and effect on the development of cities in our country, and it can also play a certain role in promoting the development of urban sculpture in our country in the future. In a modern society, 5G technology can continue to develop. Full use of this technology in the process of urban sculpture business can accurately and comprehensively analyze the current development of urban sculpture business, meet the needs of stable development of the city, provide innovations in new sculpture business, and promote the sustainable development of cities in our country. Promote the continuous progress of the city towards the direction of intelligence. This paper takes the development status and innovation transformation of urban sculpture under the background of 5G as the theoretical basis of the research, and integrates its important content to analyze and research the improvement of urban sculpture. This article takes the development of sculpture as the research object, and optimizes and improves the decision-making method of urban sculpture under the background of 5G. Through the combination of the new data analysis technology in the 5G era and the urban sculpture career, based on this, this article outlines the urban sculpture career and proposes the application of the urban sculpture career in the construction of smart cities in the 5G era. The application of 5G technology in the urban sculpture business can create many opportunities for the development of cities and sculptures by improving the development and innovation of urban sculpture business, provide many conveniences, and also help comprehensively enhance the innovation and transformation of urban sculptures. The experimental results show that this research has a better effect on improving the development and innovation of urban sculpture undertakings using the technology in the era of 5G background.
A Study on the Characteristics of Game Engines for Next-Generation Multimedia Production Education (Focusing on Unity and Unreal Game Engine)

With the development of current technology, multimedia content production requires a lot of cost and human resources, and a lot of multimedia production softwares are required. There are problems that each of these multimedia production softwares have too much to learn and takes a lot of time to produce high-quality final results. Therefore, it is difficult for college students to learn all of these within four years of college education to produce multimedia content that can be used in the working-level industry and there are many restrictions. Amid these problems, a game engine to produce games is being proposed as a new alternative. Multimedia content, which had to be made with various software, can be integrated into one game engine, and due to the nature of the game engine that can render in real time, it is possible to reduce the time to produce final results by leaps and bounds. In this paper, we want to compare the characteristics and strengths and weaknesses of each game engine with Unity and Unreal engine which are used most often among commercial game engines, and suggest an analysis of which of the above two engines is efficient in choosing and training at the university.
Poker card recognition with computer vision methods

In this paper, the face of playing cards is used as the identification target carrier. The card picture to be recognized is a 24-bit true-color image taken by a mobile phone, and then the image is preprocessed to a 256-grayscale picture, and then further converted to only 0 and 1 binary image. Finally, the contour shape matching is carried out according to the templates of hearts, spades, diamonds, clubs, and Ace-King obtained in advance. The contour shape matching is based on the Hu invariant moment. The nature of the Hu invariant moment shows that the rotation of playing cards does not affect it. Playing card recognition can assist visually impaired people to play poker games so that some people can also feel the fun of poker games and enjoy it. The playing card recognition can also realize the digital storage of cards, which can make the game fairer.
Minimum probability of error of list <i>M</i>-ary hypothesis testing

Abstract We study a variation of Bayesian $M$-ary hypothesis testing in which the test outputs a list of $L$ candidates out of the $M$ possible upon processing the observation. We study the minimum error probability of list hypothesis testing, where an error is defined as the event where the true hypothesis is not in the list output by the test. We derive two exact expressions of the minimum probability or error. The first is expressed as the error probability of a certain non-Bayesian binary hypothesis test and is reminiscent of the meta-converse bound by Polyanskiy, Poor and Verdú (2010). The second, is expressed as the tail probability of the likelihood ratio between the two distributions involved in the aforementioned non-Bayesian binary hypothesis test. Hypothesis testing, error probability, information theory.
Handwritten Text Recognition for Non-Latin Languages using Deep Learning - Bangla

Language aims to have effective handwritten recognition which purpose is writer involvement on written documents concerned with the Non-Latin language in Bangla. In the Non-Latin language, Bangla claims the necessity of Auto-Learned Features with a sufficient manner supported by Human Constructed Features. According to this problem assertion of handwritten documents, security is sought for the proposal of document identification. In a sense, the Bangla language has a large vocabulary where it renders so many complex words including compound characters. On account of the Bangl.a language, usable boundaries are large because of the volume of manpower. Therefore, handwritten documents arise a great number a couple of times. Addressing those features of the problem proposed a model with CNN where it outperformed the handwritten documents in Bangla that mixed with different writers wrote the same documents in a different way. Moreover, the SVM comparison strongly stated that CNN is an effective approach on account of 70% training and 30% testing validation concern with 83%.
Visualization of sub-network sets by iterative graph sampling from large scale networks

Multi-layer network visualization techniques have been developed so that users can firstly overview the largescale network and then explore the interesting parts of the data. Meanwhile, local features of the networks are often more interesting rather than their overall structures. It often happens with particular kinds of applications such as social networks. We developed a visualization technique for such types of large-scale networks. The technique iteratively applies a graph sampling algorithm to extract small-scale sub-networks from a large-scale network and then visualize the features of the sub-networks as hierarchically arranged icons. User-specified sub-networks are then visualized by applying our own graph visualization technique. Using networks generated from Twitter data, we actually visualize small-scale networks using the proposed method.
An Application to Default Logic

Given a default theory \((\Delta ,D),\) where \(\Delta \) is a theory in propositional logic and D is a set of defaults, we will define three kinds of the minimal change in default logic: a theory \(\Theta \) is a \(\subseteq \)-/\(\preceq \)-/\(\vdash _\preceq \)-minimal change of D by \(\Delta ,\) where \(\Theta \) is a \(\subseteq \)-minimal change of D by \(\Delta \) if and only if \(\mathrm{Th}(\Theta \cup \Delta )\) is an extension of \((\Delta ,D).\) \(\preceq \)-minimal change has the actual importance. For example, let \((\Delta ,D)\) be a default theory, where \(\Delta \) says that someone is a human (denoted by p) and this man has no arms (denoted by \(\lnot q\)); and D contains one default \(\dfrac{p:q\wedge r}{q\wedge r},\) which says that a man defaultly has arms and legs, where r denotes that this man has legs. By default logic, \((\Delta ,D)\) has an extension \(\{p\},\) that is, we know that this man is a human.
Augmenting GRIPS with Heuristic Sampling for Planning Feasible Trajectories of a Car-Like Robot

Kinodynamic motion planning for non-holomonic mobile robots is a challenging problem that is lacking a universal solution. One of the computationally efficient ways to solve it is to build a geometric path first and then transform this path into a kinematically feasible one. Gradient-informed Path Smoothing (GRIPS) [1] is a recently introduced method for such transformation. GRIPS iteratively deforms the path and adds/deletes the waypoints while trying to connect each consecutive pair of them via the provided steering function that respects the kinematic constraints. The algorithm is relatively fast but, unfortunately, does not provide any guarantees that it will succeed. In practice, it often fails to produce feasible trajectories for car-like robots with large turning radius. In this work, we introduce a range of modifications that are aimed at increasing the success rate of GRIPS for car-like robots. The main enhancement is adding an additional step that heuristically samples the waypoints along the bottleneck parts of the geometric paths (such as sharp turns). The results of the experimental evaluation provide a clear evidence that the success rate of the suggested algorithm is up to 40% higher compared to the original GRIPS and hits the bar of 90%, while its runtime is lower.
Training Person Re-identification Networks with Transferred Images

Recently, researchers begin using generated as well as transferred images to expand the training dataset of person re-identification. It expands the training dataset's sample space, which contributes to learning more robust features for the Re-ID network. However, these works only transfer a certain kind of feature between images. There is no known attempt to transfer multiple features across multiple images. By transferring multiple features, the sample space expands further, allowing Re-ID networks to learn more robust features. In this paper, we propose a unified framework and pipeline to integrate multiple feature transfer networks. Users are also free to determine how to transfer these features. Based on the above framework and pipeline, we create a large amount of transferred images from the Market-1501 dataset and create an expanded dataset named Market1501-EX. Further more, we propose a corresponding person identification labeling method for identity loss, using the generation information provided by the dataset. We use this dataset to train a Re-ID network, and the training result in this paper rests at mid-high level of related works which also utilize generated images.
On the weight distribution of perfect binary codes

In this paper, we give a new proof of the closed-form formula for the weight distribution of a perfect binary single-error-correcting code.
Toward a Robust Shape and Texture Face Descriptor for Efficient Face Recognition in the Wild

Face recognition in complex environments has attracted the attention of the research community in the last few years due to the huge difficulties that can be found in images captured in such environments. In this context, we propose to extract a robust facial description in order to improve facial recognition rate even in the presence of illumination, pose or facial expression problems. Our method uses texture descriptors, namely Mesh-LBP extracted from 3D Meshs. These extracted descriptors will then be used to train a Convolution Neural Networks (CNN) to classify facial images. Experiments on several datasets has shown that the proposed method gives promising results in terms of face recognition accuracy under pose, face expressions and illumination variation.
Gravity observation of field camp geophysics in Karangsambung using Data Acquisition 2005-2019

Abstract The study of field camp geophysics in Karangsambung has been done since 1996 until 2019 by geophysical engineering ITB. During the field activities, students was assigned with several data acquisition using various geophysical methods. One of the most common method to conducted alongside with surface geological mapping is gravity. Compilation of gravity data during the activities will be presented in this work. There are two categories of data compilation during 24 years: data compilation 1996-2004, and 2005-2019. The observation conducted using relative gravimeter with data distribution already cover geological surface map in the study area (Luk-Ulo Melange Complex, Karangsambung Formation, Totogan Formation, and Diabas Intrusion). The pattern of gravity observation shows correlated with topographic variation. Range gravity observation from this study is about 62 mGal.
Multi-scale Image Partitioning and Saliency Detection for Single Image Blind Deblurring

Solving the problem of the blurred image degraded by natural environment or human induced camera exposure has always been a challenge. The researches on blind deblurring are generally to use the statistical prior of the image as the regularization term, which can extract the critical information from the image itself. However, such methods are unable to achieve desired results because the blur kernel will be non-uniform in practice, so the inconsistencies of kernels in different image regions should be considered. Many patch-wise deblurring algorithms have certain limitations, such as the unreasonableness of partition. In this paper, we propose an adaptive multi-scale image partitioning method from coarse to fine, which provides a more accurate partition for kernel estimation locally, and uses the structural similarity value of the kernel as the criterion of partitioning. Then, we introduce the image saliency detection to obtain more edge details from the image, which can contribute to the better kernel estimation. In addition, a weighted window function is applied to the joint of image patches with different sizes to obtain the final restored image. Extensive experiments on space-invariant and space-variant blurred images demonstrate that the proposed method achieves better performance against many classical methods.
Time-frequency image-based signal recognition by using improved CenterNet

An improved CenterNet is proposed for signal recognition with time-frequency image input. The signal is transformed into time-frequency image by short-time Fourier transform, hence, the signal recognition is transformed into investigating the object detection problem in the field of image detection. Then, the advanced achievements of image detection are adopted to enhance the performance of signal recognition. Here, an improved CenterNet-based object detection network, which demonstrates great advantages in detection speed, is proposed. The results show that the proposed method identifies the signal modulation format with high speed. After training and testing on the self-collected data set with 6 types and 7800 samples, the mean average precision achieves 98.38% and the frame per second reaches 21.4. Compared with the original CenterNet, the detection speed increases more than 4 times while only reducing recognition accuracy by 0.3%, where this algorithm gives a promising way for applications of real-time signal recognition.
Extracting Academic Senses: Towards An Academic Writer’s Dictionary

We present a method for determining intended sense definitions of a given academic word in an academic keyword list. In our approach, the keyword list are converted into unigram of all possible Mandarin translations, intended or not.The method involve converting words in the keyword list into all translations using a bilingual dictionary, computing the unigram word counts of translations, and computing character counts from the word counts. At run-time, each definition (with associated translation) of the given word is scored with word and character counts, and the definition with the highest count is returned. We present a prototype system for the Academic Keyword List to generate definitions and translation for pedagogy purposes. We also experimented with clustering definition embeddings of all words and definitions, and identifying intended sense in favor of embedding in larger clusters.Preliminary evaluation shows promising performance. This endeavor is a step towards creating a full-fledged dictionary from an academic word list.
Study of the Efficiency of a Multilingual Educational Model

As new informational conditions contribute to the discovery of new ways to improve the quality of the educational process, a multilingual learning model was elaborated. A creative approach to the educational process stimulates the involvement and active participation of students, which makes the learning process less tiring. The purpose of the paper is to assess the influence of this model on students’ foreign language proficiency, formation and development of multilingualism and to examine the attitude of students towards the efficiency of such a model. For our research we used qualitative and quantitative data of students’ records of Spanish testing, multilingualism development testing and interviews on students’ attitude towards multilingual model and model efficiency from students’ perspective (N = 85). Results on students’ Spanish language proficiency showed positive impact of learning model as scores in all categories (listening, reading, writing and speaking) were substantially improved. Students’ testing results on multilingualism development were quite high (most students gave more than 50% right answers) that also confirms the positive effect of such model. According to the interview results, students support the idea of developing multilingualism, but some students believe that this should not be a mandatory university program. Also, students positively assessed the developed educational model.
Human-Robot Coordination in Agile Robot Manipulation

With the practical demands in flexible and adaptive robot manipulation skills in various environment settings, there are more challenges to be tackled to enable the robot with valid responsive behaviors in task handling. This paper discuss on the methods to achieve agile robot manipulations tasks with the advantages from human-robot teleoperation, robot perception, knowledge-based robot programming, robot motion planning and robot skill learning. Teleoperation serves as a typical Human-Robot Interaction (HRI) manner to allow the human user to guide the robot behavior in a direct manner. Robot automation, including the sensor perception (object detection, pose estimation etc.), motion planning and motion control that can handle well defined problems, but is also lack of general sense of understanding capability and not good at solving not fully defined task challenges. An agile robotic system should have a knowledge database which defines the skill sets required for the robot to handle various robot tasks. Meanwhile, the system should be able to take human assistance inputs through HRI when the robot is stuck. Moreover, the system should be able to pick up new skill set with each human knowledge input. Methodology discussion is the main scope of the paper, and preliminary experiments on telemanipulation with UR5 robot are demonstrated to show the flexible robot guidance through HRI inputs. Future work will aim to add in perception, motion planning, and picking up skill modules to come up with a more agile solution in handling variation of tasks.