Reviewerly/exHarmony huggingface dataset:
Looking for Topics: A Brief Review

This paper presents a brief review of several endeavors to identify latent variables (axes or clusters). When dealing with textual data, these latent variables (clusters or axes) are sometimes designated ex ante by the term “topic”. The first attempts to identify interpretable latent variables dates back to factor analysis at the beginning of last century. Recent years have witnessed a series of algorithmic attempts such as non-negative matrix factorization (NMF) or latent Dirichlet allocation (LDA). In the meantime, latent variables are also identified through several hybridizations and synergies of principal axes methods and clustering techniques. A single medium-sized classical corpus (Shakespeare’s 154 Sonnets) will serve as a benchmark to sketch and compare in a compact way some characteristic features of several methods.
Automated Detection of Craniofacial Landmarks on a 3D Facial Mesh

3D faces analysis has always been an active research field in computer vision and virtual reality, more specifically, detecting 3D facial landmarks automatically is of high importance step that can be used for various purposes such as maxillofacial discipline, corrective, plastic and aesthetic surgery, in which a high number of landmarks are needed to achieve more accurate detection results. The problem of accurately detecting the 3D facial landmarks has received a lot of attention, and several research studies were conducted but few of them have detected a great number automatically and precisely. In this article, we introduce a novel technique to automatically localize 30 3D facial landmarks using the geometric information extracted from the facial 3D shape. This method was tested on two 3D face databases; the Basel Face Model dataset and the sample data set from the CranioGUI dataset. The experimental results prove that the presented methodology can detect the 30 landmarks for 75% of the 3D meshes on the two databases and the detection accuracy is comparable to the results obtained by manually landmarking the facial meshes.
Integration of Fuzzy Logic in Analogical Reasoning: A Prototype

Research and practice have long been working on the development and implementation of intelligent or smart systems. Such efforts are particularly made in the field of artificial intelligence. In this context, this article deals with the design and development of a system that should come close to human information processing. The proposed framework, consisting of an analogical scheme which is extended by fuzzy logic, represents an additional information processing module that can process and interpret unknown terms by linking them with known ones. The focus of this contribution is set on the creation of a prototype able of computing conceptual and spectral analogies. Thereby, this article discusses the current state of the fuzzy analogical reasoning prototype, and its implications and limitations.
Deictic Gesture Retargeting for Telepresence Avatars in Dissimilar Object and User Arrangements

The avatar-mediated 3D telepresence system aims to enable a user in local space to interact with a remote user through a virtual avatar representing the remote user. In this system, the avatar’s movement should convey the meaning of the remote user’s movement to the local space. If the local and remote spaces have different spatial configurations, the remote user’s placement and motion should be adapted to the local space so as to preserve the motion semantics of the remote user. To this end, this paper presents a method to generate the placement and deictic gestures of the avatar. First, we develop a method to predict the probability of the avatar’s placement in the local space according to the placement of the remote user. From this, we find the optimal placement of the avatar given the configuration of the local space and the remote user’s placement. Second, we develop a simple yet effective method to retarget the remote user’s deictic gesture to his/her avatar. User study shows that our method improves user engagement and social presence in the tested telepresence scenarios.
Parameterized dictionary matching and recognition with one gap

Dictionary Matching is a variant of the Pattern Matching problem where multiple patterns are simultaneously matched to a single text. In case where the patterns contain sequences of don't care symbols, the problem is called Dictionary Matching with Gaps. The problem is related to cyber security, where the patterns represent the malware sequences we want to detect in the text, which may appear in several packets. Another famous variant of Pattern matching is the Parameterized Matching, where two equal-length strings are a parameterized match if there exists a bijection on the alphabets, such that one string matches the other under the bijection. In this paper the problem of Parameterized Dictionary Matching with One Gap is described, which is an extension of the Dictionary Matching with Gaps, where the parameterized match serves as encryption system of the malware sequences. The paper presents two algorithms solving the Parameterized Dictionary Matching with One Gap, for dictionaries with non-uniformly bounded gaps. The first solves the problem with a query time of O(|T|δmaxlog2⁡d+occ), while the second solution has a query time of O(|T|δmax+occ), where |T| is the size of the text, d is the number of gapped patterns in the dictionary, δmax is the difference between the highest upper bound and the lowest lower bound of the gaps and occ is the number of the gapped patterns reported as output. We also suggest the related problem of Parameterized Dictionary Recognition with One Gap, which requires reporting a single parameterized appearance of each gapped pattern. This is of interest in case we want only to know which malware sequences where detected in the text, and not the details of all their appearances in the text, that may be numerous. We present similar algorithms for this problem as well.
Analysis of Segregated Witness Implementation for Increasing Efficiency and Security of the Bitcoin Cryptocurrency

The purpose of this paper is to present mechanisms and algorithms for improving Bitcoin cryptocurrency efficiency, security along with the block propagation times. Specifically, Segregated Witness Implementation (SWI) issues are verified based on both the simulation and real data from the Bitcoin network. Based on the block propagation times calculated in the simulator and real-world bitcoin network, the efficiency and safety of Bitcoin has been analysed and validated.
Adaptively Leverage Unlabeled Tracklets Based on Part Attention Model for Few-Example Re-ID

Few-example learning for video person re-ID is a challenging issue. Some studies use large unlabeled samples to mine more discriminative cues to overcome the visual scarcity. But how to develop a robust model to avoid overfitting and overcome noisy labels is still a remained problem. In this letter we focus on bridging between few labeled tracklets and numerous unlabeled tracklets. We leverage unlabeled tracklets by generating pseudo labels and adaptively joins them into the training set which consists of the labeled and unlabeled data. This work is distinguished by two key contributions. First, a novel model PAM (Part Attention Model) tailored for progressive learning is proposed. It has high accuracy and fast convergence. Second, we propose a novel sampling strategy ARS (Adaptively Relative Sampling). ARS adaptively filters out noisy labels and enlarges the training set. ARS-PAM achieves significant performance gains on four mainstream datasets. On the PRID2011 and iLIDS-VID dataset, ARS-PAM reaches 89.8%, 56.1% rank-1 accuracy, which exceeds the state-of-the-art by 5.5%, 17.5% respectively.
Real-time detection and tracking of human based on image processing with laser pointing

Real-time detection and tracking of a moving human task are sorts of challenging if the camera itself is moving or place, also with tracking and detection, we are going to point the human using a laser that is present on the system itself. This paper displays an execution of real-time detection and tracking of a human with 360 degree rotating camera and a stationery camera. Adaption of various tracking of object algorithms and their effect on Execution and implementation is also presented. The system explained in this paper has a camera, which is connected to an embedded system (raspberry pi board). This embedded system or Raspberry pi board runs an image processing algorithm, which identifies a person and then keeps tracking the person as long as it's within the sight of the camera. Otherwise in the video, the person is tracked when the camera is fixed as stationery. If the object being moved, the raspberry pi gives a signal to the stepper motor to rotate the camera for 360 degree tracking and also the laser points the person simultaneously.
Biometric identification and authentication based on a new method of a pulse wave contour forming

Abstract The paper considers authentication methods, including biometric. The advantages, disadvantages and limitations of the known authentication methods are considered, and for the first time it is proposed to use hydrocuff pulse wave technology for biometric identification. A parameters set characterizing the pulse wave contour and its derivatives is defined. As the pulse wave mathematical processing result, a multiparametric feature space is formed. The paper considers the main problems that arise when collecting source data. To solve legal and ethical issues, as well as reduce the cost of forming a database, the possibility of applying algorithms for generating synthetic biometric images has been proved. Synthetic biometric images are designed to use neural network technologies for identification and authentication based on the large databases analysis.
Smartmedia

Streaming media is gaining popularity, with numerous new services for video on demand and live broadcast. These services stream requested media content to user devices like smart TVs, personal computers and smartphones. Many of these devices are mobile devices, yet streaming media services do not adapt the delivered content to the context of the request, e.g., the location of the user, the time of the request or who are the people near the viewer. In this paper we illustrate how geofencing and geoblocking can be combined with adaptive streaming media to create a new technology of contextually-adapted streaming media. The new technology has the potential to create a new type of experience for streaming media by applying real-time modification of streamed content according to the viewer and the context, e.g., modify inappropriate content when the media is played in public places, change the length of a video played by a train passenger according to a train schedule, etc. The suggested technology can also be used for preventing password sharing in a non-intrusive way and for hyperlocal geoblocking, to facilitate copyright protection. We discuss the vision of modifying streaming media in real time based on the context, elaborate on some of the challenges in implementing this vision, and present novel applications of this new technology.
Undersea Active Terrain-Aided Navigation (ATAN)

A significant limitation for Terrain Aided-Navigation requires an accurate a priori terrain map. We introduce a solution called Active Terrain-Aided Navigation that utilizes an optimal, information-theoretic architecture in conjunction with reinforcement learning to solve a stochastic dual exploration-exploitation optimization problem. The dynamic path planning solution balances competing navigational objectives of total area coverage and position localization. The exploration component uses Multiple Gaussian Process Models while the exploitation component uses a combination of a novel entropy-based approach for information discovery and prioritization coupled with a maximum a posteriori estimation technique. Simulation results and analysis for an autonomous underwater vehicle are presented.
Lesson study as a basis for mathematical practice at the university level

Abstract The aim of this paper is to present the importance of the teaching practice of the future professor of mathematics in a Colombian public university, through the application of the Japanese methodology of lesson study. This methodology is proposed as a set of skills and daily practices that the future professor of physics or mathematics must develop for the preparation of the class as proposed in the paper. Among the main results that the research found is the importance of the practice of lesson study for the teaching of science in general, with emphasis on physics and mathematics, although this methodology can also be applied to the human sciences. On the other hand, the research recognizes that the use of this method should be assumed as part of the process of formation of the student of mathematics, physics or any discipline that requires preparation of the classes for the teaching process. It contributes to the professional profile of the future teacher in terms of quality and professionalism of the teaching work. Thus, it can be concluded that, the teaching of mathematics from this approach is a positive resource in the training of the teacher of mathematics, or any discipline such as physics that wants to implement it, because, it is being reflected in the didactic work that mathematics, or any science wants to exercise in the pedagogical and didactic process of the classroom through the teaching practice and, later on, in the professional work of each probationer.
A CNN Accelerator on FPGA using Binary Weight Networks

At present, convolutional neural networks have good performance while performing the object recognition tasks, but it relies on GPUs to solve a large number of complex operations. Therefore, the hardware accelerator of the neural network has become a central topic in the hardware researchers. This letter presents the design of an FPGA-based neural network accelerator implemented on the Xilinx Zynq-7020 FPGA. We use the binary LeNet model to achieve 91% accuracy in the MNIST dataset and use binary AlexNet model to achieve 67% accuracy in the CIFAR-10 dataset. Meanwhile the hardware resource is only about 10% usage on FPGA of the original design.
MONet - Multiple Output Network for Driver Assistance Systems Based on a Monocular Camera

Deep learning based image processing has become popular and approaches using convolutional neural networks (CNNs) have been widely used in recent years. In this paper we propose a multiple output convolutional neural network for road traffic scene understanding using a monocular camera. The color images are fed into the artificial neural network that produces multiple outputs. Our model performs three tasks: semantic segmentation, object detection and vanishing point computation. The semantic segmentation produces relevant data regarding the traffic scene, the obstacle detection module provides individual obstacles, whereas the vanishing point module will provide information that can be used to perform extrinsic camera calibration. We propose a novel obstacle detection approach and extend already published work by having a vanishing point detection module. The multiple outputs are predicted in a single-step and the information can be used as an initialization step for a 3D tracking system. Our network can extract individual dynamic objects and their correlation to the 3D space can be computed using the extrinsic parameters generated from the vanishing point module.
Deep Learning Models for Gesture-controlled Drone Operation

Recently Unmanned Aerial Vehicles (UAVs) or Drones have gained enormous attention in applications like military, agriculture, industry, etc. One approach of controlling the operation of a drone is using hand gestures, which enables designing a low-cost system. However, the accuracy of such a system highly depends on the gesture recognition models. We can use a neural network-based gesture recognition model, which is a widely accepted image recognition scheme. In this work, we first design three deep neural network-based gesture recognition models: simple Convolutional Neural Networks (CNN), VGG-16, and ResNet-50 to uncover the best model for drone control. We evaluate the proposed models over our generated hand-gesture images in terms of their accuracy, precision, and complexity. The analysis reveals that each of the three models has its advantages and disadvantages while balancing between accuracy and complexity. For example, Simple CNN offers 92% accuracy on the testing set validation with the lowest validation loss compared to VGG-16 and ResNet-50. Thus, users can choose one of the proposed models to match their drone application.
Mining User’s Opinions and Emojis For Reputation Generation Using Deep Learning

Millions of people express their opinions and viewpoints about products and services directly online. Twitter is one of the popular platforms used by internet users. This massive amount of people's opinions is very relevant for automatically computing and drawing a reputation score. Therefore, having a system capable of generating a reputation value from reviews shared by the users will be a beneficial tool for companies to assess the reputation of their outputs and make favorable changes. However, a limited number of studies have inquired mining opinions expressed in natural language for reputation generation. Therefore, our approach consists of a system that generates a reputation by applying the deep learning Bidirectional Encoder Representations from Transformers (BERT) as an embedding layer and using a multi-layer Gated recurrent units (GRU) which learns from the representations produced by the transformer. Also, processing the emojis included in the opinions with a simple technique for a reliable reputation value. Experimental results conducted on two twitter datasets about different products show that our system provides the nearest reputation value to the ground truth, which is weighted average vote for each of our products provided by (IMDB and Yelp). This implies that the proposed approach can be applied in real world applications.
Research on recognition of safety helmet wearing of electric power construction personnel based on artificial intelligence technology

The traditional safety helmet wearing recognition is only based on the color, shape, texture and other characteristics of the image, which is greatly affected by the external environment, and has the problem of unstable recognition accuracy. In view of the above problems, this paper studies the recognition method of power construction workers' safety helmet based on artificial intelligence technology. After preprocessing the construction monitoring image, such as graying and denoising, the construction personnel in the detection image are located, that is, on the basis of detecting the construction personnel area, the head position of the construction personnel is located, and finally the safety helmet wearing recognition is realized by using YOLO algorithm. The simulation results show that the average recognition accuracy is 95.2%, the recognition effect is stable and has good robustness.
Improving the Accuracy of Measuring the Morphology of Red Blood Cells

The authors proposed a method for improving the accuracy of measuring the morphology of red blood cells using an advanced method of interference microscopy and subsequent digital processing of video images. These studies will expand the capabilities of laboratory blood tests to improve laboratory medical diagnostics by assessing the dynamics of changes in the morphology of red blood cells.
Handwritten digit recognition based on corner detection and convolutional neural network

Abstract In the field of inspection and testing, it is necessary to manually fill a large number of test record forms, and the digital information accounts for a large proportion in the record forms. For the purpose of automatic and accurate recognition of handwritten form data, this paper proposes a handwritten digit recognition method based on Harris corner detection algorithm and convolutional neural network(CNN). The Harris corner detection algorithm is used to identify the positioning marks in the form image, which determine the possible fill-in positions of handwritten digits. Through a 14-layer CNN model, and using the ReLu function as the excitation function, the handwritten digit features of the target area in the image are recognized. The experimental results show that the average recognition accuracy rate of this method on the test set can reach 98.14%. So a technical solution for intelligent recognition of record forms is thus provided.
Comparative Analysis of Classical and Post-quantum Digital Signature Algorithms used in Bitcoin Transactions

The use of public key cryptosystems ranges from securely encrypting bitcoin transactions and creating digital signatures for non-repudiation. The cryptographic systems security of public key depends on the complexity in solving mathematical problems. Quantum computers pose a threat to the current day algorithms used. This research presents analysis of two Hash-based Signature Schemes (MSS and W-OTS) and provides a comparative analysis of them. The comparisons are based on their efficiency as regards to their key generation, signature generation and verification time. These algorithms are compared with two classical algorithms (RSA and ECDSA) used in bitcoin transaction security. The results as shown in table II indicates that RSA key generation takes 0.2012s, signature generation takes 0.0778s and signature verification is 0.0040s. ECDSA key generation is 0.1378s, signature generation takes 0.0187s, and verification time for the signature is 0.0164s. The W-OTS key generation is 0.002s. To generate a signature in W-OTS, it takes 0.001s and verification time for the signature is 0.0002s. Lastly MSS Key generation, signature generation and verification has high values which are 16.290s, 17.474s, and 13.494s respectively. Based on the results, W-OTS is recommended for bitcoin transaction security because of its efficiency and ability to resist quantum computer attacks on the bitcoin network.
A Novel Mixed Sampling Algorithm for Imbalanced Data Based on XGBoost

In the classification processing of imbalanced datasets, the current method causes the classification surface to shift as the imbalance of sample categories, which results in the classifier's performance degradation. To this end, we propose a novel hybrid sampling ensemble algorithm (EEBS-XGBoost) based on XGBoost. The algorithm can make full use of the easily overlooked potential information in the undersampling of majority class samples; and promote the density of minority class samples in the boundary area. Finally, we get a robust classifier with better performance by building multiple balanced sample sets with which to train multiple differentiated subclassifiers. The experimental results show that this algorithm can effectively improve the classification of minority class than other methods in processing imbalanced datasets.
The Trade-Off Behaviours between Virtual and Physical Activities during COVID-19 Pandemic Period

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Encryption system based on abelian groups and rings

A simple encryption system is based on properties of abelian group an associative and commutative rings with unit is proposed. The algorithms with quadratic time complexity and memory complexity are proposed. The examples of using such system and the generalization of this system for using of gomofons are considered. To show how to appear the set of gomofons by natural way is used a simple example of massage. Tab. 5. Ref. 3 titles.