Reviewerly/exHarmony huggingface dataset:
MoRe: A Large-Scale Motorcycle Re-Identification Dataset

Motorcycles are often related to transit and criminal issues due to its abundance in the transit. Despite its importance, motorcycles are a seldom addressed problem in the computer vision community. We credit this problem to the lack of large-scale datasets and strong baseline models. Therefore, we present the first large-scale Motorcycles Re-Identification (MoRe) dataset. MoRe consists of 3,827 individuals (i.e., the set of motorbikes and motorcyclist) captured by ten surveillance cameras placed in Brazil's urban traffic scenarios. Furthermore, we evaluate a deep learning model trained using well-known training tricks from the object re-identification literature to present a strong baseline for the motorcycle re-identification (ReID) problem. More importantly, we highlight some crucial problems in this topic as the influence of distractors and the domain shift. Experimental results demonstrate the effectiveness of the strong baseline model with an increase of at least 19.27 p.p. in the rank-1 when compared to the state-of-the-art in the BPReID dataset. Finally, we present some insights regarding the information learned by the strong baseline model when computing the similarities between motorcycle images. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
A unified method for improving long-range accuracy of stereo and monocular depth estimation algorithms

Environment perception for driving applications requires very accurate sensors especially when dealing with depth measurements. LiDAR is the most trustworthy sensor in this domain, but it suffers from disadvantages in terms of number of scene points and their temporal alignment. These issues are especially relevant when dealing with long-range measurements, where each 3D point is crucial. As an alternative, in this work we focus on camera-based depth perception for objects at large distance by using stereo reconstruction and monocular depth estimation. Towards improving the capabilities of camera-based, we initially introduce a taxonomy to categorize all types of camera-based depth perception methods with respect to their long-range capabilities. We then present a correction method that works for both stereo and monocular depth perception algorithms that output a depth in discrete setting (most suitable for realtime applications). We show that our method improves the precision for such algorithms for objects at large distances without affecting the near-range accuracy. The method requires only several additional operations, preserving the real-time capabilities of the underlying algorithms.
Comparative Analysis of the Level of Development of Entrepreneurial Competencies among Students in Russia and the Czech Republic

The role of universities in the development of entrepreneurship is increasing. Within this context, universities are introducing specialized programs and disciplines aimed at developing students’ innovative and entrepreneurial competencies. This article presents the results of a comparative analysis of the level of development of students’ entrepreneurial competencies in Russia and the Czech Republic. To implement the key task, the authors analysed approaches to the classification of entrepreneurial competencies. On the basis of relevance and objectivity, the authors selected the EntreComp classification from 2016, which subsequently formed the basis for the developed methodological tools for the empirical research. The method was tested in three universities in the Russian Federation and one in the Czech Republic. The total number of participants was 436. The results of the analysis of the entrepreneurial competencies revealed significant statistical differences between the respondents from the two countries for 10 out of 15 evaluated competencies. At the same time, 68% of respondents in the Russian Federation and 64% in the Czech Republic were found to have an advanced level of development of one entrepreneurial competence. This fact is indicative of the similarity of the vast majority of the final results for the respondents from both countries and confirms the hypothesis that most students participating in the test did not have a lower than average level of competence development. The results of the study may be of interest to scientists and practitioners who study the assessment and development of entrepreneurial competencies in students and specialists.
WITHDRAWN: An approach to text analytics and text mining in multilingual natural language processing

Natural language processing (or Computational linguistic is becoming the state of art in today’s world. It has evolved many years ago in past 1960’s. The task of NLP is understanding the natural human utterances in terms of speech or text, taking as input and giving proper response or output. Text mining also called as Text Analytics uses Natural language processing to transform unstructured corpus into standard and normalised documents or databases for further analysis by applying Artificial intelligence techniques and Machine learning algorithms.
If It’s Not Secure, It Should Not Compile: Preventing DOM-Based XSS in Large-Scale Web Development with API Hardening

With tons of efforts spent on its mitigation, Cross-site scripting (XSS) remains one of the most prevalent security threats on the internet. Decades of exploitation and remediation demonstrated that code inspection and testing alone does not eliminate XSS vulnerabilities in complex web applications with a high degree of confidence. This paper introduces Google's secure-by-design engineering paradigm that effectively prevents DOM-based XSS vulnerabilities in large-scale web development. Our approach, named API hardening, enforces a series of company-wide secure coding practices. We provide a set of secure APIs to replace native DOM APIs that are prone to XSS vulnerabilities. Through a combination of type contracts and appropriate validation and escaping, the secure APIs ensure that applications based thereon are free of XSS vulnerabilities. We deploy a simple yet capable compile-time checker to guarantee that developers exclusively use our hardened APIs to interact with the DOM. We make various of efforts to scale this approach to tens of thousands of engineers without significant productivity impact. By offering rigorous tooling and consultant support, we help developers adopt the secure coding practices as seamlessly as possible. We present empirical results showing how API hardening has helped reduce the occurrences of XSS vulnerabilities in Google's enormous code base over the course of two-year deployment.
Multi-label Classification Based on Adaptive Resonance Theory

This paper proposes a multi-label classification algorithm based on an algorithm adaptation approach by applying the Adaptive Resonance Theory (ART) and the Bayesian approach for a label association process. In the proposed algorithm, the prior probability and likelihood are updated sequentially. Moreover, an ART-based clustering algorithm continually extracts useful information for multi-label classification, and holds the extracted information on prototype nodes generated by the clustering algorithm. Thanks to the above properties, the proposed algorithm can continually learn multi-label data. Our experimental results in this paper show that the proposed algorithm has better classification performance compared to typical multi-label classification algorithms.
RETRACTED: Corpus English word detection and image recognition algorithm based on improved convolutional neural network

The area recommendation for Framework is based on deep convolutional neural entities to distinguish between approach and proof. Pipeline uses a novel mix of correlation proposal creation strategies to guarantee a further review. It quickly utilizes the following filtering step to improve accuracy and recommendation positions, train heavily persuasive neural entities to give a one-time word receipt throughout the entire proposal territory, leaving out the past character classification based frameworks. These companies are made specifically on the information generated by Text Age Motor; no human name information is required. Disassembling the steps of pipeline, show the pre-execution. Picture Receipt Calculation Based on the Overall Learning Computation and Error Level Analysis (ELA) is a Complex Neural Network (CNN) scientific classification proposed to solve a problem that is difficult to correct or have an inconsistent expectation. To upgrade the whole practice's efficiency, to improve headlines' conversion, to collect top-down headlines and inconsistent headlines, the network uses an assortment of standard calculations' sample structures. The packing preparation strategy is used in the manufacturing cycle, i.e., professionals use different information indicators to verify learning inconsistencies. Text is used in Convictional Neural Networks (CNNs), relies on convoluted maps but still uses convoluted K-implies. Convulsive masks classified with near and neighboring patch features are used to improve identification accuracy. Word chart counting uses logical data to upgrade the word division and reduce bogus character word recognition. Different definitions are used for the (text) zones before preparing the innovation steps, based on the bounce box crossing point and others on the jumping box and pixel convergence.
Distributed Kalman Filter for 3-D Moving Object Tracking over Sensor Networks

This paper studies the problem of distributed state estimation (DSE) over sensor networks. Unlike the existing filtering algorithms that only consider targets moving in twodimension (2-D) environments, we address this problem in three-dimension (3-D) scenarios where each agent equipped with the communication and sensing capabilities cooperatively track the state of a 3-D moving object. First, it is shown that the existing distributed Kalman filter (DKF) algorithms cannot solve the quaternion-based six degree-of-freedom (6DoF) motion tracking. Then, a novel DKF applicable for the 3-D tracking is introduced for a general nonlinear system. The proposed algorithm is fully distributed and robust to timevarying communication topologies and changing blind agents (the agents that lose sight of the whole target object). Finally, we apply the proposed algorithm to a camera network to track the 6-DoF pose (position and orientation) of a moving target object. The effectiveness of our approach is demonstrated through Monte-Carlo simulations.
Abnormal video homework automatic detection system

Automatic abnormal detection of video homework is an effective method to improve the efficiency of homework marking. Based on the video homework review of “big data acquisition and processing project of actual combat” and other courses, this paper found some student upload their videos with poor images, face loss or abnormal video direction. However, it is time-consuming for teachers to pick out the abnormal video homework manually, which results in prompt feedback to students. This paper puts forward the AVHADS (Abnormal Video Homework Automatic Detection System). The system uses suffix and parameter identification, Open CV, and the audio classification model based on MFCC feature to realize the automatic detection and feedback of abnormal video homework. Experimental results show the AVHADS is feasible and effective.
Whose hand is this? Person Identification from Egocentric Hand Gestures

Recognizing people by faces and other biometrics has been extensively studied in computer vision. But these techniques do not work for identifying the wearer of an egocentric (first-person) camera because that person rarely (if ever) appears in their own first-person view. But while one's own face is not frequently visible, their hands are: in fact, hands are among the most common objects in one's own field of view. It is thus natural to ask whether the appearance and motion patterns of people's hands are distinctive enough to recognize them. In this paper, we systematically study the possibility of Egocentric Hand Identification (EHI) with unconstrained egocentric hand gestures. We explore several different visual cues, including color, shape, skin texture, and depth maps to identify users' hands. Extensive ablation experiments are conducted to analyze the properties of hands that are most distinctive. Finally, we show that EHI can improve generalization of other tasks, such as gesture recognition, by training adversarially to encourage these models to ignore differences between users.
SIKAP MURID TERHADAP KEMAHIRAN LITERASI DIGITAL DALAM PEMBELAJARAN BAHASA KADAZANDUSUN DI SEKOLAH MENENGAH SABAH, MALAYSIA [STUDENTS ATTITUDE TOWARDS DIGITAL LITERACY SKILL IN LEARNING KADAZANDUSUN LANGUAGE AT SECONDARY SCHOOL, SABAH, MALAYSIA]

Digital literacy skill is able to facilitate and support the students in mastery of the 21st century skills such as communication, critical thinking, collaboration and creativity. Past study shows students had a positive attitude towards the usage of computer parts and the internet in language learning. This study is conducted on the purpose of identifying the students attitude towards digital literacy in learning Kadazandusun language at secondary school, Sabah state. A quantitative study involving 265 students in Form Two undertaking the Kadazandusun language subject in eight secondary schools in Sabah state were conducted. Instrumental questionnaire of Likert scale contain ten items on attitude section and skill were done. Descriptive analysis and Spearman’s Rho correlation have been used for data analysis. Study on descriptive shows a min average for attitude is between 3.04 to 4.31 and literacy digital skill between 2.57 to 4.26. Correlation analysis shows a significant positive relationship although it is weak between the students attitude and digital literacy skill in the learning of Kadazandusun language, (rs=0.487, p&lt;0.01). This study is essential as the early observation on the preparedness of student in following the e-learning if there is no face to face in class between the teacher and students especially in the limited movement and communication during the COVID-19 pandemic.
A Monocular Forward Leading Vehicle Distance Estimation using Mobile Devices

Keeping the safe distance from the leading vehicle is crucial for transportation companies with a fleet of old cars. While modern Advanced Driver Assistant Systems (ADAS) might be able to estimate the distance from the front-leading vehicle, traditional ADAS do not usually offer this feature. An alternative solution is to monitor the distance using smartphones that are attached to a place such as a sun visor. The basic idea behind this approach is to detect the front-leading vehicle using the smartphone camera and estimate its distance from the car. Although SSD can achieve real-time performance on powerful GPUs, it remains challenging to run this model in real-time on mobile devices. In this paper, we propose a monocular distance estimator for forward-leading vehicles using a smartphone which is faster and more accurate than the state-of-the-art SSD detector. Specifically, we propose a layer-wise method to generate more efficient default boxes for the SSD and develop a lightweight method for estimating the distance accurately. Our experiments show that the proposed method reduces the number of default boxes by an average of 38.4% while it improves the detection rate and the processing speed compared to the original SSD. Moreover, our monocular distance estimator provides a proper safety buffer zone when the distance is greater than 20 meters. A sample video is available at https://youtu.be/-ptvfabBZWA.
Interwoven texture-based description of interest points in images

Local feature description is to assign a unique signature to a key-point such that it becomes distinctive from the others regardless of changes in viewpoint, illumination, rotation, scale as well as distortions and noise. This paper proposes a novel approach to construct such a descriptor. For preserving both homogeneous and heterogeneous features of a given support region, we interweave the texture information so that the key-point is more likely to be assigned a distinctive signature and neighboring key-points will be less likely to share the same texture information. The main idea behind our descriptor is to increase the areas of our observations in the given scene while the length of the local support region is fixed. Gradient magnitude and divergence, as measurement parameters of texture information, are applied to a group of pixels instead of employing a pixel-wise strategy that make the descriptor more resistant to noise, distortions and illumination variation. The required storage of the proposed descriptor is just 72 floats and its computational complexity is much lower than those of existing ones. A comparative study between the proposed method and the selected state-of-the-art ones over multiple publicly accessible datasets with different characteristics shows its superiority, robustness and computational efficiency under various geometric changes, illumination variation, distortions and noise. The code and supplementary materials can be found at https://github.com/mogvision/InterTex-Feature-Descriptor.
Top program construction and reduction for polynomial time Meta-Interpretive learning

Meta-Interpretive Learners, like most ILP systems, learn by searching for a correct hypothesis in the hypothesis space, the powerset of all constructible clauses. We show how this exponentially-growing search can be replaced by the construction of a Top program: the set of clauses in all correct hypotheses that is itself a correct hypothesis. We give an algorithm for Top program construction and show that it constructs a correct Top program in polynomial time and from a finite number of examples. We implement our algorithm in Prolog as the basis of a new MIL system, Louise, that constructs a Top program and then reduces it by removing redundant clauses. We compare Louise to the state-of-the-art search-based MIL system Metagol in experiments on grid world navigation, graph connectedness and grammar learning datasets and find that Louise improves on Metagol's predictive accuracy when the hypothesis space and the target theory are both large, or when the hypothesis space does not include a correct hypothesis because of "classification noise" in the form of mislabelled examples. When the hypothesis space or the target theory are small, Louise and Metagol perform equally well.
Decentralized Multi-agent Reinforcement Learning with Multi-time Scale of Decision Epochs

Multi-agent reinforcement learning (MARL) has attracted more and more attention in recent years. It is now widely applied in various fields, including cyber physical systems, smart grid, finance, social network, and among others. The current researches on MARL mainly focus single-time scale, in which the agents have the same decision epoch. While in real applications, it is common that the agents make decisions by different frequencies. In addition, different agents may have separate roles in the system. In this paper, we propose a more general MARL framework by introducing multi-time scale of decision epochs. We assume that agents share information with their neighbors, including state, action, and reward. The global observability of state and action, which is a common assumption, is not required. We propose a decentralized Q-learning algorithm and a modified MADDPG algorithm to solve the problem. The main contributions of this paper are as follows. First, we formulate the multi-time scale multi-agent reinforcement learning (MTMARL) problem. This provides a general framework for the related systems and problems. Second, we provide a networked decentralized multi-time scale multi-agent Q-learning algorithm to solve the problem and prove its convergence. Third, we test the algorithm numerically. The results show that the proposed algorithm performs better than the previous QD-learning and is only slightly worse than the centralized algorithm.
Dual discriminative auto-encoder network for zero shot image recognition

Zero Shot learning (ZSL) aims to use the information of seen classes to recognize unseen classes, which is achieved by transferring knowledge of the seen classes from the semantic embeddings. Since the domains of the seen and unseen classes do not overlap, most ZSL algorithms often suffer from domain shift problem. In this paper, we propose a Dual Discriminative Auto-encoder Network (DDANet), in which visual features and semantic attributes are self-encoded by using the high dimensional latent space instead of the feature space or the low dimensional semantic space. In the embedded latent space, the features are projected to both preserve their original semantic meanings and have discriminative characteristics, which are realized by applying dual semantic auto-encoder and discriminative feature embedding strategy. Moreover, the cross modal reconstruction is applied to obtain interactive information. Extensive experiments are conducted on four popular datasets and the results demonstrate the superiority of this method.
Autocompensating measurement-device-independent quantum cryptography in space division multiplexing optical fibers

Abstract Single photon or biphoton states propagating in optical fibers or in free space are affected by random perturbations and imperfections that disturb the information encoded in such states and accordingly quantum key distribution is prevented. We propose three different systems for autocompensating such random perturbations and imperfections when a measurement-device-independent protocol is used. These systems correspond to different optical fibers intended for space division multiplexing and supporting collinear modes, polarization modes or codirectional modes such as few-mode optical fibers and multicore optical fibers. Accordingly, we propose different Bell-states measurement devices located at Charlie system and present simulations that confirm the importance of autocompensation. Moreover, these types of optical fibers allow the use of several transmission channels, which compensates the reduction of the bit rate due to losses.
Long-Term Face Tracking for Crowded Video-Surveillance Scenarios

Most current multi-object trackers focus on short-term tracking, and are based on deep and complex systems that do not operate in real-time, often making them impractical for video-surveillance. In this paper, we present a longterm multi-face tracking architecture conceived for working in crowded contexts, particularly unconstrained in terms of movement and occlusions, and where the face is often the only visible part of the person. Our system benefits from advances in the fields of face detection and face recognition to achieve long-term tracking. It follows a tracking-by-detection approach, combining a fast short-term visual tracker with a novel online tracklet reconnection strategy grounded on face verification. Additionally, a correction module is included to correct past track assignments with no extra computational cost. We present a series of experiments introducing novel, specialized metrics for the evaluation of long-term tracking capabilities and a video dataset that we publicly release. Findings demonstrate that, in this context, our approach allows to obtain up to 50% longer tracks than state-of-the-art deep learning trackers.