Reviewerly/exHarmony huggingface dataset:
Task Space Planning With Complementarity Constraint-Based Obstacle Avoidance

Abstract In this paper, we present a task space-based local motion planner that incorporates collision avoidance and constraints on end-effector motion during the execution of a task. Our key technical contribution is the development of a novel kinematic state evolution model of the robot where the collision avoidance is encoded as a complementarity constraint. We show that the kinematic state evolution with collision avoidance can be represented as a Linear Complementarity Problem (LCP). Using the LCP model along with Screw Linear Interpolation (ScLERP) in SE(3), we show that it may be possible to compute a path between two given task space poses by directly moving from the start to the goal pose, even if there are potential collisions with obstacles. Scalability of the planner is demonstrated with experiments using a physical robot. We present simulation and experimental results with both collision avoidance and task constraints to show the efficacy of our approach.
Enhancing the Security and Performance of Cloud for E-Governance Infrastructure

E-Governance is getting momentous in India. Over the years, e-Governance has played a major part in every sphere of the economy. In this paper, we have proposed E-MODI (E-governance model for open distributed infrastructure) a centralized e-Governance system for government of India, the implementation of this system is technically based on open distributed infrastructure which comprises of various government bodies in one single centralized unit. Our proposed model identifies three different patterns of cloud computing which are DGC, SGC and CGC. In addition, readiness assessment of the services needs to migrate into cloud. In this paper, we propose energy efficient VM allocation algorithm to achieve higher energy efficiency in large scale cloud data centers when system on optimum mode. Our objectives have been explained in details and experiments were designed to demonstrate the robustness of the multi-layered security which is an integration of High secure lightweight block cipher CSL along with Ultra powerful BLAKE3 hashing function in order to maintain information security triad.
Composite Behavioral Modeling for Identity Theft Detection in Online Social Networks

In this work, we aim at building a bridge from coarse behavioral data to an effective, quick-response, and robust behavioral model for online identity theft detection. We concentrate on this issue in online social networks (OSNs) where users usually have composite behavioral records, consisting of multidimensional low-quality data, e.g., offline check-ins and online user-generated content (UGC). As an insightful result, we validate that there is a complementary effect among different dimensions of records for modeling users’ behavioral patterns. To deeply exploit such a complementary effect, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">joint</i> (instead of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fused</i> ) model to capture both online and offline features of a user’s composite behavior. We evaluate the proposed joint model by comparing it with typical models and their fused model on two real-world datasets: Foursquare and Yelp. The experimental results show that our model outperforms the existing ones, with the area under the receiver operating characteristic curve (AUC) values 0.956 in Foursquare and 0.947 in Yelp, respectively. Particularly, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">recall</i> (true positive rate) can reach up to 65.3% in Foursquare and 72.2% in Yelp with the corresponding <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">disturbance rate</i> (false-positive rate) below 1%. It is worth mentioning that these performances can be achieved by examining only one composite behavior, which guarantees the low response latency of our method. This study would give the cybersecurity community new insights into whether and how real-time online identity authentication can be improved via modeling users’ composite behavioral patterns.
Searching for Legal Documents at Paragraph Level: Automating Label Generation and Use of an Extended Attention Mask for Boosting Neural Models of Semantic Similarity

Searching for legal documents is a specialized Information Retrieval task that is relevant for expert users (lawyers and their assistants) and for non-expert users.By searching previous court decisions (cases), a user can better prepare the legal reasoning of a new case.Being able to search using a natural language text snippet instead of a more artificial query could help to prevent query formulation issues.Also, if semantic similarity could be modeled beyond exact lexical matches, more relevant results can be found even if the query terms don't match exactly.For this domain, we formulated a task to compare different ways of modeling semantic similarity at paragraph level, using neural and non-neural systems.We compared systems that encode the query and the search collection paragraphs as vectors, enabling the use of cosine similarity for results ranking.After building a German dataset for cases and statutes from Switzerland, and extracting citations from cases to statutes, we developed an algorithm for estimating semantic similarity at paragraph level, using a linkbased similarity method.When evaluating different systems in this way, we find that semantic similarity modeling by neural systems can be boosted with an extended attention mask that quenches noise in the inputs.
Adaptive partitioning‐based copy‐move image forgery detection using optimal enabled deep neuro‐fuzzy network

Abstract The emergence of photo editing applications, like Adobe Photoshop, has manipulated the operation of digital images into a simple task. However, these manipulations of images misrepresent the content of the original image for misleading the public. Various copy move forgery detection techniques are developed, but these show less robustness on the image with noise and blurring. This article develops an optimization‐driven deep learning technique for image forgery detection. The purpose is to develop a copy‐move image forgery detection technique using a deep neuro‐fuzzy network and a newly developed optimization algorithm. Here, adaptive partitioning is adapted using a rectangular search for splitting the image into different parts. In addition, the features like local Gabor XOR pattern and Texton features are extracted from the partition. Furthermore, the forgery is detected using the deep neuro‐fuzzy network. Finally, the deep neuro‐fuzzy network training is performed using the proposed multi‐verse invasive weed optimization (MVIWO) technique. The proposed MVIWO method will be newly designed by integrating the multi‐verse optimizer and invasive weed optimization technique. Thus, the copy‐move image forgery detection is effectively performed using the proposed MVIWO‐based deep neuro‐fuzzy network. The developed MVIWO‐based deep neuro‐fuzzy network offers superior performance with the highest specificity of 93.54%, highest accuracy of 94.01%, and highest sensitivity of 97.75%.
A Methodology to Produce Augmented-Reality Guided Tours in Museums for Mixed-Reality Headsets

In recent years, the use of technology in the museum context has changed radically. It has switched from the display of information to offering emotive, immersive, and rich experiences with heritage. Virtual interactive media have the potential to put museums back into a relevant place in our increasingly digital society. The emergence of augmented-reality glasses offers the possibility to test and implement new methodologies compatible with this aim. However, most of the first examples developed in recent years did not take advantage of the possibilities of this new medium. This paper presents a novel methodology for producing mixed-reality applications for museums and heritage sites, with an intuitive, immersive, and natural way of operating. An experimental prototype designed for the archaeological museum of the Almoina is shown in the paper to demonstrate the benefits of the proposed system and methodology of production. In addition, the paper shows the results of several tests.
Michelson interferometer for phase shifting interferometry with a liquid crystal retarder

We propose an interferometric system to measure phase objects using electronic phase shifting techniques. The experimental setup is based on a polarization Michelson type interferometer, where a liquid crystal retarder is placed at the entrance pupil as a phase shifter. First, the interferometric setup is used for the calibration of a phase-shifting device. Then, the interferometer is used to measure a phase object employing generalized phase-shifting algorithms to retrieve the phase solving the nonlinear response of the liquid crystal retarder. Experimental results are presented, and the advantages and limitations of the proposed technique are also discussed.
PSSUQ-GR: A First Step Towards Standardization of the Post-Study System Usability Questionnaire in Greek

Perceived usability is an important requirement of interactive systems, affecting greatly the overall user experience. The Post-Study System Usability Questionnaire (PSSUQ) is a popular instrument, developed in English, for assessing perceived usability of interactive systems. In this paper, a first step towards standardization of a Greek version of PSSUQ, the PSSUQ-GR, is presented. To this end, the PSSUQ was translated and adapted to Greek by two HCI experts. Next, 359 participants interacted with two websites and then completed the System Usability Scale (SUS) and the PSSUQ-GR immediately after interacting with each website. Factor analysis successfully revealed a three-factor structure of the PSSUQ-GR with 12 items and item-subscales alignment close to the original PSSUQ. Reliability analysis using Cronbach's alpha indicated that the PSSUQ-GR scale and the three subscales have a high reliability. In addition, the participants’ SUS scores and PSSUQ-GR scores were significantly and strongly correlated, which supports the validity of the PSSUQ-GR. Thus, this study found that the PSSUQ-GR (N=12 items) is a reliable and valid tool for measuring perceived usability, with a factor structure consistent with the English version of the PSSUQ.
Foundations of Feature Selection in Clinical Prediction Modeling

Selecting a set of features to include in a clinical prediction model is not always a simple task. The goals of creating parsimonious models with low complexity while, at the same time, upholding predictive performance by explaining a large proportion of the variance within the dependent variable must be balanced. With this aim, one must consider the clinical setting and what data are readily available to clinicians at specific timepoints, as well as more obvious aspects such as the availability of computational power and size of the training dataset. This chapter elucidates the importance and pitfalls in feature selection, focusing on applications in clinical prediction modeling. We demonstrate simple methods such as correlation-, significance-, and variable importance-based filtering, as well as intrinsic feature selection methods such as Lasso and tree- or rule-based methods. Finally, we focus on two algorithmic wrapper methods for feature selection that are commonly used in machine learning: Recursive Feature Elimination (RFE), which can be applied regardless of data and model type, as well as Purposeful Variable Selection as described by Hosmer and Lemeshow, specifically for generalized linear models.
Deep Embeddings-based Place Recognition Robust to Motion Blur

In this work we present an algorithm for severe (unknown) blur detection on RGB images. On salient CNN-based regional representations we calculate local features that are then fed to calibrated classifiers in order to estimate blur intensity. We perform scene classification and show that considerable gain in classification performance can be obtained owing to information on blur presence. We calculate global descriptors of the scene that are then fed to image retrieval engine that uses blur detection, scene category and minimum spanning tree to decide if current query image is relevant or irrelevant in context of place recognition. We show that information about blur and scene category improves mean average performance. We introduce a freely available challenging dataset both for blur detection and place recognition. It contains both images with severe blurs and sharp images with 6-DOF viewpoint variations, which were recorded using a humanoid robot.
Analysis of Propagation Delay Effects on Bearings-Only Fusion of Heterogeneous Sensors

In bearings-only tracking applications, the standard bearing model ignores the propagation delay of signal, except in cases where the target speed is comparable to the signal speed. This paper provides a theoretical analysis of the performance degradation suffered by a maximum likelihood estimator (MLE) that neglects the signal propagation delay in the bearings-only fusion of heterogeneous sensors: one with negligible propagation delay and the other with non-negligible delay. By using a higher order Taylor-series based analysis, we derive approximate expressions for the bias and mean square error (MSE) of the MLE. The analysis shows that neglecting the propagation delay of a sensor (with non-negligible delay) in such bearings-only fusion problems leads to severe degradation in performance even when the signal speed is orders of magnitude higher than that of target. Simulation results confirm the validity of the theoretical predictions. Finally, a bias-compensated MLE is proposed that not only takes into account the propagation delay, but also compensates for the estimation bias. This bias-compensated MLE is nearly unbiased and exhibits an RMS error performance close to the Cramer Rao lower bound.
Impact of software development processes on the outcomes of student computing projects: A tale of two universities

Project-based courses are more and more commonly used as an opportunity to teach students structured methods of developing software. Two well-known approaches in this area – traditional and Agile – have been successfully applied to drive academic projects. However too often the default is still to have no organizational process at all. While a large variety of software development life-cycle models exists, little guidance is available on which one to choose to fit the context of working with students. This paper assesses the impact of iterative, sequential and “hands-off” development approaches on the success of student computing projects. A structured, metric-based assessment scheme was applied to investigate team productivity, teamwork and the quality of the final product. Empirical evidence was collected during a controlled experiment carried out at two engineering schools in Europe. More than 100 students at Bachelor’s and Master’s levels participated in the research, with varied software development and teamwork skill sets. Similar patterns were observed among both sets of subjects, with iterative teams demonstrating the highest productivity and superior team cohesion but a decline in the quality of the final product. Sequential development led to a considerable improvement in the external quality characteristics of the software produced, owing to the method’s stress on design activities. The findings of this study will be of use to educators interested in applying software development processes to student groupwork. A set of guidelines is provided for applying a structured way of working in a project-based course.
BLS: Bank Loan Sanction Using Blockchain Authenticity, Transparency and Reliability

Blockchain technology is a decentralized ledger system that records immutable data. Nowadays blockchain is used to record, validate and secure peer-to-peer transactions. This paper focuses on how to use a blockchain network to make a partially decentralized system that we can use in banking agencies to sanction the loan. The consortium blockchain is maintained by pre-sets nodes that can improve transparency and security which is essential for the financial institution. This paper is a theoretical paper where we propose a model to validate loans and safely store the loans information in a blockchain network. The proposed model also uses the Hyperledger Fabric blockchain to implement the smart contracts method called ‘chaincodes’. The constructed system will consist of a blockchain network that can improve the reliability, efficiency, transparency, and safety of processing loans by the banking industry.
Advanced System Resiliency Based on Virtualization Techniques for IoT Devices

An increasing number of powerful devices are equipped with network connectivity and are connected to the Internet of Things (IoT). Influenced by the steady growth of computing power of the devices, the paradigm of IoT-based service deployment is expected to change, following the example of cloud-based infrastructure: An embedded platform can be provided as-a-service to several independent application service suppliers. This fosters additional challenges concerning security and isolation. At the same time, recently revealed critical vulnerabilities like Ripple20 and Amnesia:33 show that embedded devices are not spared from wide-spread attacks.
False positive repression: Data centric pipeline for object detection in brain MRI

Summary One of the problems that often arise during the application of medical research to real life is the high number of false positive cases. This situation causes experts to be warned with false alarms unnecessarily and increases their workload. This study proposes a new data centric approach to reduce bias‐based false positive predictions in brain MRI‐specific medical object detection applications. The proposed method has been tested using two different datasets: Gazi Brains 2020 and BraTS 2020, and three different deep learning‐based object detection models: Mask R‐CNN, YOLOv5, and EfficientDet. According to the results, the proposed pipeline outperformed the classical pipeline, up to 18% on the Gazi Brains 2020 dataset, and up to 24% on the BraTS 2020 dataset for mean specificity value without much change in sensitivity metric. It means that the proposed pipeline reduces false positive rates due to bias in real‐life applications and it can help to reduce the workload of experts.
Understanding feminist anticipation through ‘back-talk’: 3 narratives of willful, deviant, and care-full co-design practices

This article explores how co-design workshops might engage with the conditions that constrain our anticipation of more just futures. We discuss how practitioner commitments to feminisms might contribute to more critical exploration. Rather than exploring what practitioners should be doing in feminist futures-focused co-design, we seek a better understanding of how this practice unfolds and evolves. Therefore, the article discusses feminist anticipation from the perspective of three, first-person narratives. These accounts explore how practitioner ‘feminist tendencies’ become manifested in co-design materials. We also explore how the multi-directional ‘back-talk’ of workshop materials mediates how stakeholders, participants and the designers themselves co-anticipate contested, deviant, and plural futures. Borrowing the words from feminist scholar Sara Ahmed, these stories from practice seek to both answer and provoke the question: how are we unmaking how hard it is to deviate from what is expected?
The social basis of referential communication: Speakers construct physical reference based on listeners’ expected visual search.

A foundational assumption of human communication is that speakers should say as much as necessary, but no more. Yet, people routinely produce redundant adjectives and their propensity to do so varies cross-linguistically. Here, we propose a computational theory, whereby speakers create referential expressions designed to facilitate listeners' reference resolution, as they process words in real time. We present a computational model of our account, the Incremental Collaborative Efficiency (ICE) model, which generates referential expressions by considering listeners' real-time incremental processing and reference identification. We apply the ICE framework to physical reference, showing that listeners construct expressions designed to minimize listeners' expected visual search effort during online language processing. Our model captures a number of known effects in the literature, including cross-linguistic differences in speakers' propensity to over-specify. Moreover, the ICE model predicts graded acceptability judgments with quantitative accuracy, systematically outperforming an alternative, brevity-based model. Our findings suggest that physical reference production is best understood as driven by a collaborative goal to help the listener identify the intended referent, rather than by an egocentric effort to minimize utterance length. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Efficient Neighbor-Sampling-based GNN Training on CPU-FPGA Heterogeneous Platform

Graph neural networks (GNNs) have become increasingly important in many real-world applications. However, training GNN on large-scale real-world graphs is still challenging. Many sampling-based GNN training algorithms have been proposed to facilitate the mini-batch style training process. The wellknown Neighbor-Sampling-base (NS) GNN training algorithms, such as GraphSAGE, have shown great advantages in terms of accuracy, generalization, and scalability on large-scale graphs. Nevertheless, efficient hardware acceleration for such algorithms has not been systematically studied.In this paper, we conduct an experimental study to understand the computational characteristics of NS GNN training. The evaluation results show that neighbor sampling and feature aggregation take the majority of the execution time due to the irregular memory accesses and extensive memory traffic. Then, we propose a system design for NS GNN training by exploiting the CPU-FPGA heterogeneous platform. We develop an optimized parallel neighbor sampling implementation and an efficient FPGA accelerator to enable high-throughput GNN training. We propose the neighbor sharing and task pipelining techniques to improve the training throughput. We implement a prototype system on an FPGA-equipped server. The evaluation results demonstrate that our CPU-FPGA design achieves 12−21× speedup than CPU-only platform and 0.4 − 3.2× speedup than CPU-GPU platform. Moreover, our FPGA accelerator are 2.3× more energy efficient than the GPU board.
A Full-Sample Clustering Model Considering Whole Process Optimization of Data

With the continuous increase of data volume and data dimensions, it becomes more and more difficult to improve the accuracy and interpretability of the algorithm only from the clustering algorithm itself. In order to improve the accuracy of the clustering algorithm and improve the interpretability of the clustering results, we propose an Improved feature selection and combined clustering model considering whole process optimization. In this model, we processed the data from the whole process of data mining and carried out clustering analysis. Firstly, we started data preprocessing, and then used the feature selection algorithm of text weight + principal component analysis (PCA) to reduce the feature dimension and obtain important features and data sets for clustering. Secondly, we used the improved Self organizing maps (SOM) neural network and K-means clustering combination model to perform clustering analysis and established clustering algorithm evaluation indicators. Thirdly, we used collaborative filtering to cluster data sets that included missing data to ensure that all sample data can obtain results. Finally, through case analysis, it was verified that the model proposed in this paper had high clustering accuracy and interpretability.