Reviewerly/exHarmony huggingface dataset:
Cultivating Practitioners for Software Engineering Experiments in industry

Controlled experiments are commonly used to evaluate software methods, processes, and tools. Literature presents that the validation of software engineering research results in industrial settings is a powerful way to obtain feedback about its value. However, to implicate industry and practitioners in experiments is not an easy task, and, even when a company is committed, frequently the number of practitioners involved is not enough to execute and validate the experiment. This chapter presents a guide of best practices that can be used in order to get a high number of participants that can validate research results from the industry. These practices are oriented to create an attractive environment for companies to conclude that the participation in a research experiment can be interesting for them. In order to illustrate them, the chapter also introduces the authors' experiences when running an experiment about early testing at the University of Seville, where they followed these guidelines to successfully enhance the participation of 76 practitioners from 32 different software companies.
Multi-object Tracking Combines Motion and Visual Information

Real-time online multi-object tracking is a fundamental task in video analysis applications. A major challenge in the tracking by detection paradigm is how to deal with missing detections. Visual single object trackers (SOTs) have been introduced to make up for the poor detectors in the assumption of appearance continuity of tracking objects. However, visual SOTs may easily be confused by the invaded foreground when occlusion occurs. In this paper, we propose to combine object motion information and appearance feature to improve the performance of object tracker. We use a lightweight re-identification feature to monitor occlusion. A Kalman filter, as the motion predictor, and a visual SOT, as the appearance model are worked together to estimate the new position of the occluded object. Experimental evaluation on MOT17 dataset shows that our online tracker reduces the number of ID switches by 26.5% and improves MOTA by 1–2% compared to the base intersection-over-union (IOU) tracker. The effectiveness of our method is also verified on MOT16 datasets. At the same time, the tracking speed can reach 29.4 fps which can basically achieve real-time tracking requirement while ensuring accuracy.
Towards Multimodal Sentiment Analysis Inspired by the Quantum Theoretical Framework

In this paper, we report on a quantum theoretical frame-work that constructs a multimodal representation for the input video sequence and infers the users' sentiments regarding a textual query-based topic. Furthermore, we report on the experimental results of the use of our quantum theoretical framework also in comparison with the state-of-the-art methods. Finally, we describe the design and the implementation of a prototype which incorporates the proposed methods.
Probabilistic path planning using current obstacle position in static environment

In current scenario so many real time application based on predefined path has been introduced who decides the future movement of an heavy crowded traffic in a current scenario & the analysis of this heavily crowded surrounding (area) is a most complex and challenging task in visual monitoring system and hence important key points should be kept into account such as current environmental surrounding, current and future obstructions, and obstacles. These application based on a mobile robot or an any intelligent device that can be seen in different fields like medical field, agricultural fields, science and research field, commercial field, engineering field, industries, defense, transportation field etc. The movement of intelligent device is sometimes predefined or may be changed during the real time task hence the navigation plays a very important role. Predicting the future path needs various data like real time environmental surrounding of the destination, current state of an destination etc. Also the path finding techniques and obstacle avoidance is key point while discussing the various navigational approaches. Here in this paper we try to systematically summarize the various methods of path prediction of future navigation of an target in a crowded environment. We systematically summarize the various methods of path prediction for navigation of target. In this we have presented a multifold probabilistic path planning algorithm along with its based function for the implementation of algorithm. Finally we have systematically represented all the steps obtained by implementing the algorithm in the form of coordinate system generated results. We also motioned the conclusion of the work carried out.
(Machine) learning parameter regions

How many random points from an identified set, a confidence set, or a highest posterior density set suffice to describe them? This paper argues that taking random draws from a parameter region in order to approximate its shape is a supervised learning problem (analogous to sampling pixels of an image to recognize it). Misclassification error – a common criterion in machine learning – provides an off-the-shelf tool to assess the quality of a given approximation. We say a parameter region can be learned if there is an algorithm that yields a misclassification error of at most ϵ with probability at least 1−δ, regardless of the sampling distribution. We show that learning a parameter region is possible if and only if its potential shapes are not too complex. Moreover, the tightest band that contains a d-dimensional parameter region is always learnable from the inside (in a sense we make precise), with at least max(1−ϵ)ln1∕δ,(3∕16)d∕ϵ draws, but at most min{2dln(2d∕δ),exp(1)(2d+ln(1∕δ))}∕ϵ. These bounds grow linearly in the dimension of the parameter region, and are uniform with respect to its true shape. We illustrate the usefulness of our results using structural vector autoregressions. We show how many orthogonal matrices are necessary/sufficient to evaluate the impulse responses’ identified set and how many ‘shotgun plots’ to report when conducting joint inference on impulse responses.
3D Face Recognition using Photometric Stereo and Deep Learning

Illumination variance is one of the largest real-world problems when deploying face recognition systems. Over the last few years much work has gone into the development of novel 3D face recognition methods to overcome this issue. Photometric stereo is a well-established 3D reconstruction technique capable of recovering the normals and albedo of a surface. Although it provides a way to obtain 3D data, the amount of training data available captured using photometric stereo often does not provide sufficient modelling capacity for training state-of-the-art feature extractors, such as deep convolutional neural networks, from scratch.
Stereo Matching with Multi‐scale Based on Anisotropic Match Cost

Abstract Binocular vision is a popular method for 3d measurement, but the precision and efficiency is still a limitation. In order to solve the problem of low precision and high mismatching rate of current stereo match methods in weak texture region, a local stereo matching algorithm with multi‐scale based on anisotropic match cost was proposed. Not only including the gray value between the adjacent pixels as the absolute difference (AD) method, the matching cost function also involved gradient and phase information to make an anisotropic evaluation and eliminate the outliers of the flat area. Meanwhile, a multi‐scale strategy with the image pyramid was referenced, and the dynamic disparity search range of variable window was used on the original cost aggregation framework by improving the stereo matching algorithm of cross‐scale cost aggregation. The matching cost volume in each scale space used the dynamic support window to guide the filter to aggregate the matching cost. Finally, to overcome the problems of disparity selection ambiguity of WTA (winner‐take‐all) strategy and the horizontal fringe introduced by left‐right consistency (LRC) detection, the weighted median filter based on guided filter weight was used to carry out disparity refinement. According to the experiments, the method got more high matching accuracy, and the average error on the Middlebury test platform reached 5.25%.
Quantum walk inspired algorithm for graph similarity and isomorphism

Large-scale complex systems, such as social networks, electrical power grid, database structure, consumption pattern or brain connectivity, are often modelled using network graphs. Valuable insight can be gained by measuring similarity between network graphs in order to make quantitative comparisons. Since these networks can be very large, scalability and efficiency of the algorithm are key concerns. More importantly, for graphs with unknown labelling, this graph similarity problem requires exponential time to solve using existing algorithms. In this paper, we propose a quantum walk inspired algorithm, which provides a solution to the graph similarity problem without prior knowledge on graph labelling. This algorithm is capable of distinguishing between minor structural differences, such as between strongly regular graphs with the same parameters. The algorithm has a polynomial complexity, scaling with $$O(n^9)$$ .
SemBRS: A Semantic Analysis Based Book Retrieval Approach

The manner of keyword-based retrieval in today's libraries cannot achieve precise retrieval of books, which normally requires an understanding of the book contents. This paper proposes SemBRS, a retrieval system that enables book searching using queries regarding the key contents of books. In SemBRS, a multi-layer sequence-to-sequence model of semantic parsing is designed for analyzing queries in natural language to be structured retrieval statements. Relevant books are obtained based on the correlation of the parsed query information and the tags of books. As the fundamental work, the tags of each book are generated based on an analysis of the contents of the book using a multi-label classification model.
Facial Expression Recognition using Low Level Histogram Features

The proposed framework recognizes facial expressions with the minimal histogram features. Emotional parts of the face are traced with the combination of face detection and facial landmark techniques; HOG integrated with a linear Support Vector Machine [SVM] classifier framework is adopted for face detection. The landmark points are aligned by using an ensemble of regression algorithm. The aligned facial landmark points are integrated by using a Delaunay triangulation. The mask is created by means of the convex hull points. The emotional parts of the face are isolated by the mask. For feature extraction, HOG and LDP descriptors are utilized to derive the distinct features of the detected face area. The dimensions of the fused feature vectors are reduced with the use of single-layer auto-encoder. The downsized features are recognized by using the soft-max layer. Facial expressions are recognized from still images and video frames. Images are collected from JAFFE, WSEFEP datasets, RAVEES audio-visual database and some of the images are collected from internet. The performance of the proposed model attains 95.7%.
Recognition of FAST reflector nodes based on Canny operator

Abstract The reflector system of Five-hundred-meter Aperture Spherical radio Telescope (FAST) is designed as 4450 rigid panels supported by a flexible cable-net structure. We use 10 total stations to measure 2225 nodes of the cable-net and then control the shape of the reflectors. Every time, it takes at least 35 minutes to finish the calibration of the whole cable-net once. It is indeed far too inefficient. Thus, we developed a set of highly efficient instrument CRRS (CCD Rotation Ranging System). It is based on photogrammetry and can finish the measurement in 1 minute. However, the target we used in CRRS is active target, and it has serious electromagnetic interference problems to affect the use of FAST. Take the above reasons into consideration, we plan to identify the nodes by taking the gap between the reflector panels as the feature condition. The new method can take the place of active targets to finish the measuring task. The present work focuses on the following aspects. First, combined with the characteristics of FAST reflector images, the representative algorithms of image edge detection are discussed. Second, the process of node extraction is introduced in detail so that we know that it works. In addition, experimental results are given to draw a conclusion so that Canny algorithm was used for continuous research of reflector edge detection.
Research on the Key- frame Extraction of Surveillance Video Based on Motion Velocity

People pay more attention to the change of object motion in surveillance video. Therefore, this paper proposes a key frame extraction method based on motion velocity. This method firstly extracts the moving object, next calculates the velocity of object and processes the velocity, then generates the motion velocity curve of the object, and finally extracts the key frames of the surveillance video according to the curve. Experimental results reveal that the proposed method can accurately extract the video frames whose motion changes in the process of object motion.
TheChain

The Smart Distributed Ledger (aka blockchain) has attracted much attention in recent years. According to the European Parliament, this technology has the potential to change the lives of many people. The blockchain is a data structure built upon a hashed function in a distributed network, enabled by an incentive mechanism to discourage malicious nodes from participation. The consensus is at the core of the blockchain technology, and is driven by information embedded into a data structure that takes many forms such as linear, tree, and graph chains. The found related information will be subject to various validation incentives among the miners, such as proof of stake and proof of work. However, all the existing solutions suffer from a heavy state transition before dealing with the problem of a validation mechanism which suffers from resource consumption, monopoly or attacks. This work raises the following question: "Why is there a need for consensus where all participants can make a quick and correct decision?", and underlines the fact that sometimes ledger is subject to maintenance from regional parties in the data that leads to partial territories and eliminates monopoly, which is the hurdle to eliminating the trusted party. The validity of the blockchain transaction comes from the related information scattered above the data structure, and the authenticity lies in the digital signature. The aim is to switch from a validator based on incentives to a broadcaster governed by an unsupervised clustering algorithm, and the integrity does lie in the intersection among regions. However, the data structure takes advantage of the Petri network regarding its suitability. Building the entire ledger in the Petri network model will allow parallel processing of the transactions and securing of the total order between the participants on the memory reference layer. Moreover, it takes account of validation criteria quickly and safely before adding the new transaction list using the graph reachability.
Locality sensitive hashing for tampering detection in automotive systems

In modern auto vehicles we find dozens of Electronic Control Units (ECUs) running several hundred MBs of code, alongside sophisticated dashboards with integrated wireless communications. While this technological advancement has brought upon a wide range of advantages and integrated features, it also exposed the modern vehicle to significant cyber threats, as documented in prior works. Unfortunately, besides traditional cyber attacks, the security and normal operation of the modern vehicle are nowadays exposed to a different kind of threat. This is the tampering, which denotes a procedure that alters the vehicle's behavior in order to gain particular advantages (e.g., financial, operational). A fundamental distinction between tampering and cyber attacks, is that tampering occurs with the owner's consent. This paper presents an approach for detecting tampering within modern vehicles by leveraging the advantages of sensitive hashing, namely the Exact Euclidean Locality Sensitive Hashing (E2LSH) method. Experimental results based on a dataset collected from the On-Board Diagnostics port (OBD) of a Kia SOUL vehicle demonstrate the practical applicability of the developed methodology.
Detection and Removal of Salt and Pepper Noise by Gaussian Membership Function and Guided Filter

The performance of vision-based algorithms depends on the quality of digital images. The images corrupt with salt and pepper noise during image acquisition and transmission deteriorate the performance of these algorithms. This generates the necessity of enhancement algorithms for noise removal. This paper presents an approach for salt and pepper noise removal from digital images. Firstly, the corrupt pixels are detected using a Gaussian membership function and then denoising of these corrupt pixels is performed by a combination of Gaussian and Guided filter. A digital image contains visual information in the form of pixel intensities and regions with abrupt intensity changes. The image regions with similar pixel intensities are called homogeneous and the regions with abrupt intensity changes are called edges or textures. These regions are responsible for carrying important image details. The objective of image denoising is to retrieve the actual pixel intensities in such regions. The proposed approach aims to identify and denoise the corrupt pixels by salt and pepper noise such that the details present in homogeneous regions and edges remain unchanged. For detection of corrupt pixels, two thresholds are estimated using Gaussian membership function. Then, a combination of Gaussian and Guided filter is used for denoising these detected corrupt pixels. The Gaussian filter helps to assign proper weights to the neighborhood pixel set for averaging. However, the Guided filter helps to maintain the structure of an image at very high noise level. The experiments are performed on standard images used in literature with different noise levels up to 99%. It shows that the proposed approach performs efficiently in terms of peak signal to noise ratio.
How Does a Total Reward System Influence Employee Motivation Among Executive Management? An Analysis of the UAE Real Estate Industry

This chapter reviews the impact of a total reward system on employee motivation among executive management in the real estate industry of the GCC, particularly in the context of the UAE, based on a survey conducted from a sample of 250 executive-level employees working in real estate firms. The measurable responses are unwrapped in this research analysed on the basis of a 5-point Likert scale, and the secondary data has been analysed by using Statistical Package for the Social Sciences (SPSS). The regression analysis, descriptive statistics and correlation results show that monetary rewards have a positive relationship with the motivation level of executive employees working in real estate firms. Moreover, non-monetary rewards also have a positive relationship with the motivation level of these employees. On the basis of these findings, the author offers recommendations for organizations to enhance the motivation level of their executive employees.
Teaching Cryptography Using CYPHER (InteraCtive CrYPtograpHic Protocol TEaching and LeaRning)

The lack of cryptographic knowledge is one factor contributing to the cybersecurity skills gap. Cryptography is the core of the research in data security and a fundamental component in information assurance yet complex topic, with its purpose to conceal and authenticate information. Hence, teaching cryptography-based security protocols in an effective and engaging manner is a challenging problem throughout international HE sectors. With explanations usually based around mathematical notations, it can be a daunting and difficult subject for many students. This point is emphasized by the consistently lower marks achieved, in comparison to other course modules. To combat this, we propose the CYPHER platform, which delivers a motivating and engaging practical element to teaching and learning popular cryptographic protocols. It customizes a real-world scenarios that are illustrated by animation videos to depict and simplify a number of well-known cryptographic protocols. CYPHER provides an interactive learning experience, allowing the breakdown of complex topics to a level understood by each student. By providing assessment gamification, the delivered knowledge will be assessed in a fun way by playing a game to pass a number of stages and measure the level of understanding. The analysis of the evaluations' results showed that over 88% of the students surveyed had found the CYPHER platform increased the overall speed of understanding.
Private Set Intersection Using Multi-Message Symmetric Private Information Retrieval

We study the problem of private set intersection (PSI). In PSI, there are two entities, each storing a set Pi, whose elements are picked from a finite set S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</sub> , on N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> replicated and non-colluding databases. It is required to determine the set intersection P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ∩P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> without leaking any information about the remaining elements to the other entity. We first show that the PSI problem can be recast as a multi-message symmetric private information retrieval (MM-SPIR) problem. Next, as a stand-alone result, we show that the exact capacity of MM-SPIR is C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MM-SPIR</sub> = 1 - 1/N when P ≤ K - 1, if the common randomness S satisfies H(S) ≥ P/N-1 per desired symbol. This result implies that there is no gain for MM-SPIR over successive single-message SPIR. We present a novel capacity-achieving scheme which builds seamlessly over the multi-message PIR (MM-PIR) scheme. Based on this capacity result for the MM-SPIR problem, we show that the optimal download cost for the PSI problem is given by min{[P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> /N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -1],[P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> /N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -1]}, where P i is the cardinality of the set Pi.