Reviewerly/exHarmony huggingface dataset:
Software Project Management Education: A Systematic Review

Software project management (SPM) is a significant field, related to the discipline of software engineering, which has attracted a huge number of researchers and practitioners in recent years. The role of software project management education (SPME) is essential to instill appropriate competencies in SPM students so that they could be able to deliver effectively in the software industry as SPM professionals. As the demand of SPM professionals is continuously increasing, the responsibilities of academia have also been increased to ensure and provide the solid basis and quality education to SPM. In this context, a number of methods and tools have been deliberated to examine SPME from various dimensions. The usefulness of these methods and tools need to be synthesized and evaluated to investigate the areas that could be used to improve the various aspects of SPME. The main objective of this research is to evaluate the techniques that have been applied to teach SPME and identify the future directions that could lead to make improvements in SPME. This article presents a review of eighty-nine research papers of SPME that have been classified and synthesized according to different aspects of SPME. These aspects include type and approach of conducted research, empirical type, SPM knowledge areas, and curricula. The principal findings emerged after analyzing the results of reviewing the SPME research have been discussed in detail. Lastly, a list of advice to SPM instructors, based on the prominent and effective practices of SPME, has also been presented in this study.
Development of Open Source-based Threat Hunting Platform

Threat hunting is a process that focuses on activities that are repeated in nature, by taking an approach to identify and understand threat actors who may have entered and are in the computer network infrastructure. It becomes the focus of the Central Security Operation Centers (SOCs) as a survey conducted by the SANS Institute on threat hunting, show 91% of enterprises reported increasing the implementation of threat hunting. Therefore, this research attempts to design a threat hunting platform using Elasticsearch, Logstash, and Kibana (ELK) by implementing rules and alerts obtained from sigma rules for attack detection and performing penetration testing using the Atomic Red Team method and Web Application Vulnerability to obtain attack logs. In the log mapping process, an analysis of the previously obtained attack detection was carried out. Then we create a threat hunting framework using MITRE ATT&CK, Pyramid of pain and Diamond Models of Intrusion Analysis. By getting the results of attack detection from 10 rules and alerts detected on ELK and obtaining 3 tactics and 4 attack techniques on the attack method with the Atomic Red Team, and on the Web Application Vulnerability we discover 2 tactics and 3 attacks.
Non-local Network Routing for Perceptual Image Super-Resolution

In this paper, we propose a non-local network routing (NNR) approach for perceptual image super-resolution. Unlike conventional methods which generate visually-faked textures due to exiting hand-designed losses, our approach aims to globally optimize both procedures of learning an optimal perceptual loss and routing a spatial-adaptive network architecture in a unified reinforcement learning framework. To this end, we introduce a reward function to teach our objective to pay more attention on the visual quality of the super-resolved image. Moreover, we carefully design an offset operation inside the neural architecture search space, which typically deforms the receptive field on boundary refinement in a non-local manner. Experimentally, our proposed method surpasses the perceptual performance over state-of-the-art methods on several widely-evaluated benchmark datasets.
Distracted driving detection by sensing the hand gripping of the phone

Phone usage while driving is unanimously considered a really dangerous habit due to a strong correlation with road accidents. This paper proposes a phone-use monitoring system that detects the driver's handheld phone use and eliminates the distraction at once. Specifically, the proposed system emits periodic ultrasonic pulses to sense if the phone is being held in hand or placed on support surfaces (e.g., seat and cup holder) by capturing the unique signal interference resulted from the contact object's damping, reflection and refraction. We derive the short-time Fourier transform from the microphone data to describe such impacts and develop a CNN-based binary classifier to discriminate the phone use between the handheld and the handsfree status. Additionally, we design a classification error correction filter to correct the classification errors during the monitoring. The experiments with six people, one phone and one car model show that our system achieves 99% accuracy in recognizing handheld phone-use activities.
A New Variance-Covariance Matrix for Improving Positioning Accuracy in High-Speed GPS Receivers

One of the main challenges in using GPS is reducing the positioning accuracy in high-speed conditions. In this contribution, by considering the effect of spatial correlation between observations in estimating the covariances, we propose a model for determining the variance-covariance matrix (VCM) that improves the positioning accuracy without increasing the computational load. In addition, we compare the performance of the extended Kalman filter (EKF) and unscented Kalman filter (UKF) combined with different dynamic models, along with the proposed VCM in GPS positioning at high speeds. To review and test the methods, we used six motion scenarios with different speeds from medium to high and examined the positioning accuracy of the methods and some of their statistical characteristics. The simulation results demonstrate that the EKF algorithm based on the Gauss-Markov model, along with the proposed VCM (based on the sinusoidal function and considering spatial correlations), performs better and provides at least 30% improvement in the positioning, compared to the other methods.
GroupNet: Detecting the Social Distancing Violation using Object Tracking in Crowdscene

COVID-19 affects everyone on a daily-basis causing adjustments in which society functions. One of these major adjustments is the need to measure how well people distance from each other, that is referred to as social distancing. Previous work to automate social distancing violations does not take into consideration the exceptions to minimum distance guidelines. In this paper, we propose GroupNet, a novel multi-object tracking social distancing violation detector through the addition of group detection to reduce the number of false positives that are currently missed in existing literature. We define the social distancing violation occurs when two individuals are within a specified Euclidean distance of two meters. GroupNet leverages the contextual information learned by group detection. Moreover, GroupNet uses a Joint Detection and Embedding (JDE) multi-object tracker as a backbone network for group detection. To map from pixel-wise coordinates to the real-world equivalent coordinate, a pre-processed affine matrix is used for the transformation. GroupNet determines if two individuals are a group through leveraging a re-identification component from a multi-object tracker. Location of bounding boxes are tracked over time to obtain individuals relative distance between each other. Group-Net uses regression analysis to determine if the relative distance between two individuals changes over time. The likelihood of the change in relative distance is non-zero determines group existence between individuals (i.e. group detection). Performance of GroupNet is evaluated by manual inspection of output images due to the lack of labeled ground truth data. Moreover, GroupNet through the provided experiments shows an improvement in the reduction in the number of false-positives due to group detection analysis alongside the addition of minimal false-negatives.
Why LASSO Seems to Simultaneously Decrease Bias and Variance in Machine Learning

We show that on an enhancement of the capacity of the function space used in regression, LASSO simultaneously decreases bias and variance of statistical models obtained in machine learning from training data, if the balance between minimization of the mean-squared error and the L1-regularization term is optimal. Further, if minimization of the mean-squared error is dominant, this seems to explain the occurrence of a double descent in the modern interpolation regime of machine learning. Our main method is a decomposition of mean squared error plus complexity into bias, variance and an unavoidable irreducible error inherent to the problem.
Javanese Script Recognition based on Metric, Eccentricity and Local Binary Pattern

Handwriting recognition is one of the most interesting researches in computer vision. Some previous research has developed and implemented Javanese script recognition in digital fonts and handwritten, but handwriting recognition is still not optimal. The contribution to this research is to improve the recognition accuracy in handwritten Javanese scripts. The proposed method is to combine metric feature extraction, eccentricity, and local binary pattern (LBP) which is further classified with k-Nearest Neighbor (KNN). Several preprocessing stages are carried out so that the features are extracted optimally. After testing the proposed method succeeded in improving recognition performance with 92.5% accuracy, 92.5% recall, and 100% precision on 200 training data and 40 testing data.
SEDML: Securely and efficiently harnessing distributed knowledge in machine learning

• Securely and efficiently harnessing distributed knowledge in machine learning. • Lightweight secure aggregation of teacher ensembles based on secret sharing. • Differential privacy guarantees for individual participants contributing knowledge. Training high-performing machine learning models require a rich amount of data which is usually distributed among multiple data sources in practice. Simply centralizing these multi-sourced data for training would raise critical security and privacy concerns, and might be prohibited given the increasingly strict data regulations. To resolve the tension between privacy and data utilization in distributed learning, a machine learning framework called private aggregation of teacher ensembles (PATE) has been recently proposed. PATE harnesses the knowledge (label predictions for an unlabeled dataset) from distributed teacher models to train a student model, obviating access to distributed datasets. Despite being enticing, PATE does not offer protection for the individual label predictions from teacher models, which still entails privacy risks. In this paper, we propose SEDML, a new protocol which allows to securely and efficiently harness the distributed knowledge in machine learning. SEDML builds on lightweight cryptography and provides strong protection for the individual label predictions, as well as differential privacy guarantees on the aggregation results. Extensive evaluations show that while providing privacy protection, SEDML preserves the accuracy as in the plaintext baseline. Meanwhile, SEDML outperforms the state-of-the-art work of Xiang et al. (ICDCS’20) by 43 × in computation and 1.23 × in communication.
Grounded Vocabulary for Image Retrieval Using a Modified Multi-Generator Generative Adversarial Network

With the recent increase in requirement of both natural-language and visual information, the demand for research on seamless multi-modal processing for effective retrieval of these types of information has increased. However, because of the unstructured nature of images, it is difficult to retrieve images that accurately represent the input text. In this study, we utilized an augmented version of a multi-generator generative adversarial network that uses BERT embeddings and attention maps as input to enable grounded vocabulary for visual representations. We compared the performance of our proposed model with those of other state-of-the-art text input-based image retrieval methods on the MSCOCO and Flikr30K datasets, and the results showed the potential of our proposed method. Even with limited vocabulary, our proposed model was comparable to other state-of-the-art performances on R@10 or even exceed them in R@1. Moreover, we revealed the unique properties of our method by demonstrating how it could perform successfully even when using more descriptive text or short sentences as input.
Fintech and Blockchain

The rapidly growing field of Blockchain and Decentralised Finance (DeFi) has the potential to transform many aspects of the financial world. It offers an abundance of opportunities to reduce costs, increase transparency and reduce the need for middlemen in financial services. While this promise of automation and decentralization is attractive, it is important to consider the potential for inadvertent or deliberate automation of unethical conduct at scale. Ethical questions involve the consideration of conflicting moral choices and dilemmas. Blockchain creates ethical dilemmas for developers, investors, consumers, and regulators at the technology, application, and societal levels. This chapter provides a perspective on the emerging field of DeFi and Blockchain in financial services, a reflection on the ethical questions that arise, how they are being addressed, the key issues, and further research needed in this growing field of Blockchain ethics. The objective of the chapter is for students, developers, CEO's and governments to appreciate the moral and ethical issues being uncovered in the course of the development and deployment of Blockchain technology. Blockchain, as with all technology, is a tool and is as beneficial and useful as the care that is taken to make it. There remains a need to ensure that Blockchains are built and deployed with due concern for ethics.
CNN Real-Time Detection of Vandalism Using a Hybrid -LSTM Deep Learning Neural Networks

Vandalism is a deliberate damage to property by humans and it has become rampant in the engineering fields. The activity results into huge financial and social loses and the vice is declared when human image is detected in the restricted area without authority to cause an unauthorized change in a predetermined scene that could be vandalized. This act requires an automated real-time detection of the presence of the vandal so that he can be stopped from damaging the property. Human Image recognition process is the best method for detection of vandalism. In this research paper, we propose a deep learning architecture combining Convolutional Neural Networks and Long Short Term memory (CNN-LSTM) which has the ability to exhaust spatial relationship and temporal prediction of the output. The main objective of this research work is to develop, train, test and validate CNN-LSTM against CNN and LSTM models to prove the superiority of the proposed model in image recognition. Image detection is achieved by feeding the images captured by installed image sensors (CCD camera) to a hybrid neural network classifier which is trained to recognize human images. The CNN-LSTM hybrid approach not only improves the predictive accuracy of image recognition from raw data but also reduces the computational complexity. The model is trained and tested with image-Net dataset which is the largest clean image dataset for vision research. Results show that the proposed model is able to achieve a training accuracy of 98% while a standalone CNN achieved 88%. The result show that the hybrid model is superior.
On Computing the Hyperparameter of Extreme Learning Machines: Algorithm and Application to Computational PDEs, and Comparison with Classical and High-Order Finite Elements

We consider the use of extreme learning machines (ELM) for computational partial differential equations (PDE). In ELM the hidden-layer coefficients in the neural network are assigned to random values generated on [âˆ’Rm,Rm] and fixed, where Rm is a user-provided constant, and the output-layer coefficients are trained by a linear or nonlinear least squares computation. We present a method for computing the optimal or near-optimal value of Rm based on the differential evolution algorithm. The presented method enables us to illuminate the characteristics of the optimal Rm for two types of ELM configurations: (i) Single-Rm-ELM, corresponding to the conventional ELM method in which a single Rm is used for generating the random coefficients in all the hidden layers, and (ii) Multi-Rm-ELM, corresponding to a modified ELM method in which multiple Rm constants are involved with each used for generating the random coefficients of a different hidden layer. We adopt the optimal Rm from this method and also incorporate other improvements into the ELM implementation. In particular, here we compute all the differential operators involving the output fields of the last hidden layer by a forward-mode auto-differentiation, as opposed to the reverse-mode auto-differentiation in a previous work. These improvements significantly reduce the network training time and enhance the ELM performance. We systematically compare the computational performance of the current improved ELM with that of the finite element method (FEM), both the classical second-order FEM and the high-order FEM with Lagrange elements of higher degrees, for solving a number of linear and nonlinear PDEs. It is shown that the current improved ELM far outperforms the classical FEM. Its computational performance is comparable to that of the high-order FEM for smaller problem sizes, and for larger problem sizes the ELM markedly outperforms the high-order FEM.
Investigating Impact of Augmented Reality on Game Design to Facilitate Learning Experiences in Logistics Operations Using Immersive AR Interfaces

Augmented Reality (AR) provides an immersive experience platform where players may interact with the real world. Virtual digital information details enhance this interaction with the physical environment. This paper focuses on investigating using Augmented Reality (AR) technology for player’s interaction influences the game design (technical features and interactivity aspects mainly) that could facilitate its players to achieve its proposed Intended Learning Objectives when playing different scenarios for logistic operations. To achieve this, an initial mock-up is set up with simple AR interactions to analyse the overall immersive player’s experience.
Fast Projector-Driven Structured Light Matching in Sub-pixel Accuracy Using Bilinear Interpolation Assumption

In practical applications where high-precision reconstructions are required, whether for quality control or damage assessment, structured light reconstruction is often the method of choice. It allows to achieve dense point correspondences over the entire scene independently of any object texture. The optimal matches between images with respect to an encoded surface point are usually not on pixel but on sub-pixel level. Common matching techniques that look for pixel-to-pixel correspondences between camera and projector often lead to noisy results that must be subsequently smoothed. The method presented here allows to find optimal sub-pixel positions for each projector pixel in a single pass and thus requires minimal computational effort. For this purpose, the quadrilateral regions containing the sub-pixels are extracted. The convexity of these quads and their consistency in terms of topological properties can be guaranteed during runtime. Subsequently, an explicit formulation of the optimal sub-pixel position within each quad is derived, using bilinear interpolation, and the permanent existence of a valid solution is proven. In this way, an easy-to-use procedure arises that matches any number of cameras in a structured light setup with high accuracy and low complexity. Due to the ensured topological properties, exceptionally smooth, highly precise, uniformly sampled matches with almost no outliers are achieved. The point correspondences obtained do not only have an enormously positive effect on the accuracy of reconstructed point clouds and resulting meshes, but are also extremely valuable for auto-calibrations calculated from them.
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K-means clustering is one of the most widely-used partitioning algorithm in cluster analysis due to its simplicity and computational efficiency, but it may not provide ideal clustering results when applying to data with non-spherically shaped clusters. By considering the asymmetrically weighted loss, we propose the K-expectile clustering and search the clusters via a greedy algorithm that minimizes the within cluster τ -variance. We provide algorithms based on two schemes: the fixed τ clustering, and the adaptive τ clustering. Validated by simulation results, our method has enhanced performance on data with asymmetric shaped clusters or clusters with a complicated structure. Applications of our method show that the fixed τ clustering can bring some flexibility on segmentation with a decent accuracy, while the adaptive τ clustering may yield better performance. All calculation can be redone via quantlet.com.
Variational Deep Representation Learning for Cross-Modal Retrieval

In this paper, we propose a variational deep representation learning (VDRL) approach for cross-modal retrieval. Numerous existing methods map the image and text to the point representations, which is challenging to model the semantic multiplicity of the sample. To address this issue, our VDRL aims to map the image and text to the semantic distributions and measure the similarity by comparing the difference between their distributions. Specifically, our VDRL network is trained under three constraints: 1) The Variational Autoencoder loss is minimized to learn the distributions of both the images in image semantic space and the texts in text semantic space. 2) The mutual information is introduced to ensure the VDRL learns the intact distribution for the sample. 3) The triplet hinge loss is incorporated to align the distributions of the images and texts at the semantic level. Consequently, the semantic multiplicity of each sample is modeled in our method. Experimental results demonstrate that our approach achieves compelling performance with state-of-the-art methods.
GramMatch: An automatic protocol feature extraction and identification system

Network protocol identification, or traffic classification, plays a key role in field of network monitoring, management, and optimization. Deep Packet Inspection (DPI) technology is the most popular and effective way of protocol identification. However, the accuracy of deep packet inspection often depends on the selection of protocol features, which is a complex task. To cope with the ever-increasing types of network protocols and identify traffic of them, we propose a basic model of protocol traces, and propose GramMatch, an automatic protocol feature extraction and identification system based on the model. It first aligns packets in the protocol flows by similarity with order, and then uses n-gram’s statistical features for keyword segmentation and gets keywords’ correlated characteristic as the protocol features. Finally, it performs protocol identification based on features extracted. We test GramMatch on eleven commonly used protocols and compare it with other algorithms and DPI programs. Our results prove that GramMatch is an effective, broadly applicable and better protocol feature extraction and identification system which can identify the network traces of a protocol with a weighted precision reached up to 99.81%, and a weighted recall reached up to 98.21%.