Reviewerly/exHarmony huggingface dataset:
Robust Person Tracking Algorithm Based on Convolutional Neural Network for Indoor Video Surveillance Systems

In this paper, we present an algorithm for multi person tracking in indoor surveillance systems based on tracking-by-detection approach. Convolutional Neural Networks (CNNs) for detection and tracking both are used. CNN Yolov3 has been utilized as detector. Person features extraction is performed based on modified CNN ResNet. Proposed architecture includes 29 convolutional and one fully connected layer. Hungarian algorithm is applied for objects association. After that object visibility in the frame is determined based on CNN and color features. For algorithm evaluation prepared videos that was labeled and tested using MOT evaluation metric. The proposed algorithm efficiency is illustrated and confirmed by our experimental results.
ROS-based Model Predictive Trajectory Tracking Control Architecture using LiDAR-Based Mapping and Hybrid A Planning

For the past few decades, autonomous vehicles had been one of the most highlighted fields of attraction to the industrial and research communities. The creation of an autonomous ground vehicle has many challenges. Accordingly, the target of this paper is showing a holistic view over the architecture of an autonomous ground vehicle with the focus on experimental offline mapping and path planning using Normal Distribution Transform (NDT) mapping technique and hybrid A* state lattice technique respectively, in addition to the implementation of Model Predictive Control (MPC) for trajectory tracking control of a golf cart. The proposed techniques were successfully verified for their purposes.
2-D Scanning Galvanometer Error Analysis and Its Correction

Abstract The 2-D Scanning Galvanometer is an optical scanner with high precision, high reliability and high repeatability. It is also widely used. In order to eliminate the nonlinear scanning error of the 2-D Scanning Galvanometer system, in order to improve the scanning precision of the system, the theoretical distortion is derived, and the geometric distortion and various errors in the 2-D Scanning Galvanometer system are analyzed in detail. The targeted coordinate transformation method is used to correct the distortion error. The experiment proves that the method can significantly improve the scanning precision of the rapid prototyping machine.
Multicopy uncertainty observable inducing a symplectic-invariant uncertainty relation in position and momentum phase space

We define an uncertainty observable acting on several replicas of a continuous-variable state, whose measurement induces phase-space uncertainty relations for a single copy of the state. By exploiting the Schwinger representation of angular momenta in terms of bosonic operators, this observable can be constructed so as to be invariant under symplectic transformations (rotation and squeezing in phase space). We first design a two-copy uncertainty observable, which is a discrete-spectrum operator vanishing with certainty if and only if it is applied on (two replicas of) any pure Gaussian state centered at the origin. The non-negativity of its variance translates into the Schr\"odinger-Robertson uncertainty relation. We then extend our construction to a three-copy uncertainty observable, which exhibits additional invariance under displacements (translations in phase space) so that it vanishes on every pure Gaussian state. The resulting invariance under all Gaussian unitaries makes this observable a natural tool to capture the phase-space uncertainty (or the deviation from pure Gaussianity) of continuous-variable bosonic states. In particular, it suggests that the Shannon entropy associated with the measurement of this observable provides a symplectic-invariant entropic measure of uncertainty in position-momentum phase space.
A Context-Driven Framework for Selecting Mental Model Elicitation Methods

Mental models describe an internal representation of knowledge of an individual or group, which can be used to interpret interactions with their environment and provide insight into decision-making strategies and prediction of performance. There are several ways to elicit mental models and analyze them; however, there is little guidance for selecting an appropriate elicitation method. Depending on different constraints of research and desired outcomes, different elicitation methods are more appropriate than others. Three criteria were identified as useful for selecting an appropriate elicitation method. These were the interaction level with participants, the number of participants being evaluated, and the resulting level of analytical detail that is required. A process for selecting the most appropriate mental model elicitation method is herein presented. Additionally, an overview of the factors that affect the selection of the mental models, and the different types of mental models are also presented.
Group Decision Making with Collaborative-Filtering ‘in the loop’: interaction-based preference and trust elicitation

Recommender systems have been one of the main methods to overcome the information overload problem in many domains including tourism. A solo traveller may easily create an itinerary according to his or her taste. However, when it comes to a group of people, it is often difficult to find the most suitable places to please everyone's preferences, not only because of lack of prior information about a place but also owing to the difficulty to take influence among group members into account. In our work, we introduce a model for Group Decision Making that uses YouTube API to gather rich video contents associated with a finite set of alternatives, e.g. travel destinations. Instead of eliciting subjective opinions on alternatives directly, preferences are built upon a Collaborative Filtering approach, based on each participant's watch history and interaction with items. Randomly chosen videos from different group members are also recommended to each target user in order to infer trust information within the group. We then use trust information to obtain an aggregated group preference for determining places to visit. An application example based on YouTube API shows that a higher degree of interaction consolidates a trust network, resulting in informed decision results.
Optimization of Fuzzy Controllers for Autonomous Mobile Robots Using the Grey Wolf Optimizer

Through the advance of technology, every day new methods or computational techniques emerge that allow us to solve problems in different areas, such as medicine, engineering, even in any industrial process. Optimization is of vital importance in this industry, the main objective being to find the best possible solution to the problem. In this work we propose to use the Grey Wolf Optimizer (GWO), which is a metaheuristic, which is inspired by the hunting behavior and leadership hierarchy of grey wolves, in addition to analyzing and explaining the proposed methodology for the optimization of fuzzy controllers for mobile autonomous robots.
A Systematic Mapping Study of Value-Based Software Engineering

Integrating value-oriented perspectives into the principles and practices of software engineering is critical to ensure that software development and management activities address all key stakeholders' views and also balance short-and-long-term goals. This is put forward in the discipline of Value-Based Software Engineering (VBSE). In this paper, a mapping study of VBSE is detailed. We classify evidence on VBSE principles and practices, research methods, and the research types. This mapping study includes 134 studies located from online searches, and backward snowballing of references. Our results show that VB Requirements Engineering (22%) and VB Planning and Control (19%) were the two principles and practices mostly investigated in the VBSE literature, whereas VB Risk Management, VB People Management and Value Creation (3% respectively) were the three less researched. In terms of the research method, the most commonly employed method is case-study research. In terms of research types, most of the studies (28%) proposed solution technique(s) without empirical validation.
Real-time Tracking-by-Detection of Human Motion in RGB-D Camera Networks

This paper presents a novel real-time tracking system capable of improving body pose estimation algorithms in distributed camera networks. The first stage of our approach introduces a linear Kalman filter operating at the body joints level, used to fuse single-view body poses coming from different detection nodes of the network and to ensure temporal consistency between them. The second stage, instead, refines the Kalman filter estimates by fitting a hierarchical model of the human body having constrained link sizes in order to ensure the physical consistency of the tracking. The effectiveness of the proposed approach is demonstrated through a broad experimental validation, performed on a set of sequences whose ground truth references are generated by a commercial marker-based motion capture system. The obtained results show how the proposed system outperforms the considered state-of-the-art approaches, granting accurate and reliable estimates. Moreover, the developed methodology constrains neither the number of persons to track, nor the number, position, synchronization, frame-rate, and manufacturer of the RGB-D cameras used. Finally, the real-time performances of the system are of paramount importance for a large number of real-world applications.
Atomic Cross-Chain Swaps with Improved Space and Local Time Complexity

An effective atomic cross-chain swap protocol is introduced by Herlihy [Herlihy, 2018] as a distributed coordination protocol in order to exchange assets across multiple blockchains among multiple parties. An atomic cross-chain swap protocol guarantees; (1) if all parties conform to the protocol, then all assets are exchanged among parties, (2)even if some parties or coalitions of parties deviate from the protocol, no party conforming to the protocol suffers a loss, and (3) no coalition has an incentive to deviate from the protocol. Herlihy [Herlihy, 2018] invented this protocol by using hashed timelock contracts. A cross-chain swap is modeled as a directed graph $$D=(V,A)$$. Vertex set V denotes a set of parties and arc set A denotes a set of proposed asset transfers. Herlihy's protocol uses the graph topology and signature information to set appropriate hashed timelock contracts. The space complexity of the protocol (i.e., the total number of bits written in the blockchains in a swap) is $$O(|A|^2)$$. The local time complexity of the protocol (i.e., the maximum execution time of a contract in a swap to transfer the corresponding asset) is $$O(|V|\cdot |L|)$$, where L is a feedback vertex set computed by the protocol. We propose a new atomic cross-chain swap protocol which uses only signature information and improves the space complexity to $$O(|A|\cdot |V|)$$ and the local time complexity to O(|V|).
High Dynamic Range Imaging Using A 2x2 Camera Array with Polarizing Filters

Stepping into the 4.0 industry, digital cameras play an important role in most activities of our daily life. They are considered to be the eyes of intelligent machines such as autonomous vehicles, industrial sorting machines, etc. In many situations with difficult lighting conditions, conventional cameras cannot capture objects and phenomena correctly due to lack of dynamic ranges. Many researchers have used multiple captures which involved taking many photos with different exposure levels to achieve high dynamic range (HDR) images [1-7]. Consequently, it is difficult to record HDR motion picture. To overcome this difficulty, we propose a technique of a single-shot recording of HDR image using a 2x2 polarization imaging camera. Each camera is an imaging device equipped with a polarizing filter having a different linear-polarized light from the object. The proposed technique synthesizes an HDR image from the multiple images acquired from the cameras. We compare our results with the traditional approach using multiple exposures for cases with different lighting conditions. The results are promising.
Decision support system with TOPSIS method for lecturer appraisal in Universitas PGRI Madiun

Abstract Assessment of lecturer at university is a form of activity to evaluate the performance of each lecturer. PGRI University of Madiun (UNIPMA) always strives to improve accreditation by improving quality with other universities. Making a decision support system is one way to assist in determining lecturer’s assessment in UNIPMA. This decision support system uses the TOPSIS method and will be developed using the Waterfall model. In this study to solve the problem of educator performance assessment that is UNIPMA lecturer. The assessment process includes qualifications of education, learning, research, and the number of community service activities that have been done. By using the TOPSIS method of counting process and giving the result of lecturer appraisal that has been roughed more efficient and precise. Based on the results of the research using lecturer data sampling of 10 educators obtained the highest value data is 0.75 (Very Good) where the range of decision assessment 1 - 0.65 (Very Good), 0.64 - 0.4 (Good), 0.39 - 0.25 (Enough) and 0.24 - 0 (Bad).
PR Product: A Substitute for Inner Product in Neural Networks

In this paper, we analyze the inner product of weight vector w and data vector x in neural networks from the perspective of vector orthogonal decomposition and prove that the direction gradient of w decreases with the angle between them close to 0 or π. We propose the Projection and Rejection Product (PR Product) to make the direction gradient of w independent of the angle and consistently larger than the one in standard inner product while keeping the forward propagation identical. As a reliable substitute for standard inner product, the PR Product can be applied into many existing deep learning modules, so we develop the PR Product version of fully connected layer, convolutional layer and LSTM layer. In static image classification, the experiments on CIFAR10 and CIFAR100 datasets demonstrate that the PR Product can robustly enhance the ability of various state-of-the-art classification networks. On the task of image captioning, even without any bells and whistles, our PR Product version of captioning model can compete or outperform the state-of-the-art models on MS COCO dataset. Code has been made available at: https://github.com/wzn0828/PR_Product.
Smart Communications in Heterogeneous Spacecraft Networks: A Blockchain Based Secure Auction Approach

In the forthcoming space communications, there would be a large number of spacecrafts that belong to different organizations or countries. How to efficiently allocate spectrum resources for such heterogeneous spacecraft networks becomes a very important and challenging issue. While spectrum auction provides a potential solution to spectrum allocation, how to preserve the privacy during the auction process in the spacecraft networks has not been well studied yet. In this paper, we propose a secure spectrum auction scheme by utilizing blockchain and cryptography technologies, which can preserve bidders' identity and bid privacy, and protect the auctions against collusion attacks. We evaluate our method with well-designed experiments and demonstrate its effectiveness and practicability.
Using Mono and Stereo Camera System for Static and Moving Objects Detection

Obstacle detection is a very important component for an autonomous vehicle, but since it is still a long way until this type of vehicle can be legally and safely present on the public roads, we can focus on a system that can aid the driver during long trips in order to make sure that he will get safe at his destination.
Attention Prediction on Webpage Images using Multilabel Classification

Image elements (called webpage images) are prominent in drawing the user attention on webpages. Quantitatively predicting the attention on webpage images help in their synthesis as well as rendering. This paper presents the attention prediction as a multilabel classification problem for the webpage images. Firstly, fixated images were assigned multiple quantified visual attention (called fixation-indices) based on users' sequential attention allocation on webpages. Subsequently, fixated images' visual features such as intensity, brightness, color histograms, position and size on respective webpages, were extracted. A multilabel classification problem formulated using the visual features and fixation-indices was solved using multiple binary support vector machine (binary-SVM) classifiers. The performance of the proposed approach was analyzed through a free-viewing eye-tracking experiment conducted on 36 real-world webpages with 42 participants. Our model outperforms the baseline with precision, recall, Fl-score, and an accuracy of 84.14%, 78.91%, 76.57%, 67.49% respectively with a hamming loss of 23.62% and a subset 0/1 loss of 66.50%.
Social and pedagogical planning of teachers’ professional development in educational organization

The rapid changes of the modern world, their strength, scale, dynamism and variability are essential characteristics of contemporary reality. They relate to all spheres of human activity and, thus, determine the need for changes in education, as well as understanding the current system of training and professional development of teaching staff. In the context of life-long learning, teachers’ further education and professional development become crucial for their career. The intraschool system of professional development is based on the potential of the teaching staff, resources of the educational organization, unique characteristics of the counterparts and the society’s potential. It is flexible, mobile, quickly responding to the needs and demands of the society and is able to take into account the distinctive features and conditions of a modern educational organization. Modern educational organizations are able to educate school teaching staff with due regard to their professional experience and needs of the educational organization and the society. This article presents the results of the research of teachers’ professional development at their workplace. The authors consider various approaches to the issues of teachers’ professional development, identify components, criteria and indicators of professional development, justify the possibilities and stages of social and pedagogical planning as the basis for the professional development of teachers and describe the model for designing an individual trajectory of teachers’ professional development at the place of employment
Research on Pedestrian Detection Algorithm Based on Image

Abstract Pedestrian detection is an important application of target detection. It is an important foundation in many fields, such as unmanned vehicle driving, intelligent monitoring system, pedestrian analysis and robot development. Despite a lot of research, pedestrian detection methods still have many problems to be solved urgently. In order to sort out the existing pedestrian detection algorithms, this paper will summarize and analyze the advantages and disadvantages of these algorithms. The two methods of traditional algorithms and deep learning algorithms are used to discuss pedestrian detection, analyze and compare the results of the two types of algorithms, and finally target pedestrians. The detection algorithm is expected.
An Efficient Algorithm for Mining Frequent Closed Inter- Transaction Patterns

One of the key tasks of data mining is frequent pattern mining (FPM), discovering patterns that frequently occur in transactions of transaction databases. A majority of methods of FPM only mine patterns in the same transactions, they do not consider patterns across several transactions (intertransaction patterns- ITPs) in a transaction database. So far, a number of algorithms have been proposed to mine ITPs. However, they still have many problems that need improving to make them more efficient in mining time and memory usage. This paper presents an efficient method of mining frequent closed inter-transaction patterns (FCITP) from a transaction database. Our proposed method generates inter-transaction patterns at the 1-pattern level. Besides, we propose an efficient pruning strategic to prune infrequent inter-transaction 1- patterns and quickly generate frequent inter-transaction 2- patterns. To eliminate non-closed patterns, the proposed method adopts properties of frequent closed patterns and pruning strategies. In addition, a Diffset-based strategy for the efficient computation of the supports of closed inter-transaction patterns is described. Experiment results conducted on several datasets of various characteristics show that our algorithm outperforms the state-of-the-art algorithms in terms of mining time and memory usage in most cases.
Bayesian Classifier Applied to Higher Education Dropout

The research proposes a new simple Bayesian classifier (SBND) with Markov from the class variable to a network structure. Experimental tests are carried out by working a dropout analysis on students enrolled in the Faculty of Engineering Sciences of Mumbai University, in India in the period 2017-2018 on the basis of socioeconomic data. The Weka tool is then used to perform the classification and the proposed model is statistically compared with other Bayesian classifiers.
Heterogeneous multirobot cleaning system: State and parameter estimation

This paper presents a heterogeneous multirobot cleaning system, Jacana, and its performance test with respect to state and parameter estimation. This system aims for autonomous cleaning by robots with limited capabilities and consists of a cleaning robot for wet cleaning, a sensing one for gathering robots' and environmental data, and a server PC for processing the collected data. For the system, the feasibility of the state and parameters estimation of the cleaning robot is demonstrated with experimental data by applying the unscented Kalman filter (UKF) with a system model considering the kinematics and input transition of the cleaning robot and observation by the sensing one. The results show that the proposed system can adequately estimate the state and parameters of the cleaning robot, although the system also requires improvement in autonomous cleaning. Consequently, the proposed system has the capability to accomplish autonomous cleaning with less acquisition costs and high flexibility.
Attention Neural Baby Talk: Captioning of Risk Factors while Driving

Driving has various risk factors, including the possibility of traffic accidents involving pedestrians and/or oncoming vehicles. A driver assistance system that can prevent traffic accidents must be able to get the driver ' s attention. A practical solution for attention attraction should involve caption generation from in-vehicle images. Although a number of approaches for caption generation with deep neural networks have been proposed, they are inadequate for the specific risk factors while driving. The reason is that conventional captioning methods focus on not these factors but the entirety of an image. To tackle this problem, we first created a dataset to attract attention, one that considers risk factors during driving. Furthermore, we propose an image captioning method for the assistance system. Our method is based on neural baby talk and introduces an attention mask focusing on risk factors in an image. The mask enables our model to generate captions on each factor. Experimental results with our created dataset show that our method can generate captions for ideal attention attraction.