Reviewerly/exHarmony huggingface dataset:
An Enhanced Quality of Service-Based Trust Model for Cloud Computing

Cloud computing has taken the computing services market by storm due to its advantages over traditional computing platforms. Cloud computing makes its resources available over the Internet, so clients can purchase them and pay only for what is used similar to other utilities. For cloud computing to become acceptable to a wider business clientele including the ones with high expectations, it is vital to maintain the QoS commitments. Hence, it is necessary to have mechanisms and tools to measure and quantify the performance of cloud service providers. In this paper, the authors propose a mathematical model that could measure multiple QoS attributes, quantify them and compute a single score called the trust score to help customers identify the most suitable service provider. The proposed enhanced QoS (eQoS) trust model was tested using simulations. The results show that the proposed mechanism performs better than the other two QoS-based trust models, namely QoS Trust and Turnaround Trust published in the literature.
Phase measuring profilometry based on binary grating projection for fringe order self-extraction

A phase measuring profilometry (PMP) based on binary grating projection for fringe order self-extraction is proposed. In traditional PMP based on sinusoidal grating projection, the phase is wrapped between (−π, π] due to the arctangent operation. And wrapped phase needs to be unwrapped using relative phase unwrapping algorithms such as diamond-type phase unwrapping algorithm in which the accumulation of phase errors is easy to occur or even lead to wrong phase unwrapping; or with the assistance of some additional structured light patterns such as Gray code, the fringe order may be determined to achieve phase unwrapping without the phase error accumulation. In the proposed method, three-dimensional measurement is achieved by just projecting a set of phase-shifted binary gratings, which can not only avoid the gamma non-linearity influence of the projector, but also increase the refresh frequency of the projector by more than ten times. At the same time, by using the image characteristics of the binary grating itself, the fringe order can be extracted without increasing any additional structured light pattern. So that the phase unwrapping can be achieved by combining the phase of extracted fringe order with the wrapped phase without the phase error accumulation. The experimental results verify the feasibility and effectiveness of the proposed method, and the solution result is in good agreement with the solution result of the diamond algorithm without cumulative errors.
Fast-MD: Fast Multi-Decoder End-to-End Speech Translation with Non-Autoregressive Hidden Intermediates

The multi-decoder (MD) end-to-end speech translation model has demonstrated high translation quality by searching for better intermediate automatic speech recognition (ASR) decoder states as hidden intermediates (HI). It is a two-pass decoding model decomposing the overall task into ASR and machine translation sub-tasks. However, the decoding speed is not fast enough for real-world applications because it conducts beam search for both sub-tasks during inference. We propose Fast-MD, a fast MD model that generates HI by non-autoregressive (NAR) decoding based on connectionist temporal classification (CTC) outputs followed by an ASR decoder. We investigated two types of NAR HI: (1) parallel HI by using an autoregressive Transformer ASR decoder and (2) masked HI by using Mask-CTC, which combines CTC and the conditional masked language model. To reduce a mismatch in the ASR decoder between teacher-forcing during training and conditioning on CTC outputs during testing, we also propose sampling CTC outputs during training. Experimental evaluations on three corpora show that Fast-MD achieved about 2× and 4× faster decoding speed than that of the naïve MD model on GPU and CPU with comparable translation quality. Adopting the Conformer encoder and intermediate CTC loss further boosts its quality without sacrificing decoding speed.
Forecasting the survival rate of breast cancer patients using a supervised learning method

The paper aims to develop a regression model using the NKI breast cancer data set. The methodology used to achieve the objectives includes three variations of regression methods viz., linear, multiple, and polynomial, respectively. Regression analysis is one of the efficient predictive modeling methods that help understand the mathematical relationship between the variables. The multiple and polynomial regression methods also work in line with the linear regression model, but the number of independent variables will be varying. Queries related to health care data are of practical interest. The outcome of the predictive model helps in analyzing the behavior of different features of the breast cancer data set and provides useful insights towards the diagnosis of a patient. 14 out of 1570 useful features of the NKI data set are selected for the regression analysis. With different combinations of independent and dependent variables, it is found that multiple regression performs better with 83% accuracy.
UlyssesNER-Br: A Corpus of Brazilian Legislative Documents for Named Entity Recognition

The amount of legislative documents produced within the past decade has risen dramatically, making it difficult for law practitioners to consult and update legislation. Named Entity Recognition (NER) systems have the untapped potential to extract information from legal documents, which can improve information retrieval and decision-making processes. We introduce the UlyssesNER-Br, a corpus of Brazilian Legislative Documents for NER with quality baselines. The presented corpus consists of bills and legislative consultations from Brazilian Chamber of Deputies. We implemented Conditional Random Field (CRF) and Hidden Markov Model (HMM) models, and the promising F1-score of 80.8% in the analysis by categories and 81.04% in the analysis by types, was achieved with the CRF model. The entities with the best average F1-score results were “FUNDlei” and “DATA”, and the ones with the worst results were “EVENTO” and “PESSOAgrupoind”. The corpus was also evaluated using a BiLSTM-CRF and Glove architectures provided by the pioneering state-of-the-art paper, achieving F1-score of 76.89% in the analysis by categories and 59.67% in the analysis by types.
Teachers’ Perceptions of Their Role and Classroom Management Practices in a Technology Rich Primary School Classroom

This case study investigates primary school teachers’ perceptions of their role and practices regarding classroom management in technology-rich classrooms. The data was collected through individual and focus group interviews, observation and a survey at a school where implementation of digital technologies has been a high priority over several years. The study identifies complexity and contemporary elements in teachers’ perceived role and practices, as the rapid evolution of ICT requires teachers to constantly keep up-to-date, gain new competencies and evaluate their practices to be able to facilitate learning in physical classrooms that have expanded to the digital space. In this process, the role of leadership, collegial collaboration, good teacher-pupil relationships and teachers’ ability to adapt and take up a role of a learner have been found pivotal.
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation

Transcripts generated by automatic speech recognition (ASR) systems for spoken documents lack structural annotations such as paragraphs, significantly reducing their readability. Automatically predicting paragraph segmentation for spoken documents may both improve readability and downstream NLP performance such as summarization and machine reading comprehension. We propose a sequence model with self-adaptive sliding window for accurate and efficient paragraph segmentation. We also propose an approach to exploit pho-netic information, which significantly improves robustness of spoken document segmentation to ASR errors. Evaluations are conducted on the English Wiki-727K document seg-mentation benchmark, a Chinese Wikipedia-based document segmentation dataset we created, and an in-house Chinese spoken document dataset. Our proposed model outperforms the state-of-the-art (SOTA) model based on the same BERT-Base, increasing segmentation F1 on the English benchmark by 4.2 points and on Chinese datasets by 4.3-10.1 points, while reducing inference time to less than 1/6 of inference time of the current SOTA.
Varying-coefficient hidden Markov models with zero-effect regions

In psychological, social, behavioral, and medical studies, hidden Markov models (HMMs) have been extensively applied to the simultaneous modeling of longitudinal observations and the underlying dynamic transition process. However, the existing HMMs mainly focus on constant-coefficient HMMs. This study considers a varying-coefficient HMM, which enables simultaneous investigation of the dynamic covariate effects and between-state transitions. Moreover, a soft-thresholding operator is introduced to detect zero-effect regions of the coefficient functions. A full Bayesian approach with a hybird Markov chain Monte Carlo algorithm that combines B-spline approximation and penalization technique is developed for statistical inference. The empirical performance of the propose method is evaluated through simulation studies. An application to a study on the Alzheimer's Disease Neuroimaging Initiative dataset is presented.
bAdvertisement: Attacking Advanced Driver-Assistance Systems Using Print Advertisements

In this paper, we present bAdvertisement, a novel attack method against advanced driver-assistance systems (ADASs). bAdvertisement is performed as a supply chain attack via a compromised computer in a printing house, by embedding a “phantom” object in a print advertisement. When the compromised print advertisement is observed by an ADAS in a passing car, an undesired reaction is triggered from the ADAS. We analyze state-of-the-art object detectors and show that they do not take color or context into account in object detection. Our validation of these findings on Mobileye 630 PRO shows that this ADAS also fails to take color or context into account. Then, we show how an attacker can take advantage of these findings to execute an attack on a commercial ADAS, by embedding a phantom road sign in a print advertisement, which causes a car equipped with Mobileye 630 PRO to trigger a false notification to slow down. Finally, we discuss multiple countermeasures which can be deployed in order to mitigate the effect of our proposed attack.
Image Encryption Algorithm Based on New Fractional Beta Chaotic Maps

In this study, a new algorithm of fractional beta chaotic maps is proposed to generate chaotic sequences for image encryption.The proposed technique generates multi random sequences by shuffling the image pixel position.This technique is used to blur the pixels connecting the input and encrypted images and to increase the attack resistance.The proposed algorithm makes the encryption process sophisticated by using fractional chaotic maps, which hold the properties of pseudo-randomness.The fractional beta sequences are utilized to alter the image pixels to decryption attacks.The experimental results proved that the proposed image encryption algorithm successfully encrypted and decrypted the images with the same keys.The output findings indicate that our proposed algorithm has good entropy and low correlation coefficients.This translates to enhanced security against different attacks.A MATLAB programming tool was used to implement and assess the image quality measures.A comparison with other image encryption techniques regarding the visual inspection and signal-to-noise ratio is provided.
A Convolutional Neural Network With Multi-scale Kernel and Feature Fusion for sEMG-based Gesture Recognition

The sEMG-based gesture recognition has great potential in human-computer interaction. However, the current approaches are far from optimal. In this paper, a novel convolutional neural network which combines the multi-scale kernal and feature fusion (MKFF-CNN) is proposed. This model could extract multi-scale features and make full use of these feature maps. One dataset called “gForce dataset” is recorded in this work, comprising 13 able-bodied participants. To verify the effectiveness of the proposed model, evaluations on both the gForce dataset and the Ninapro DB6 are conducted. The experimental results demonstrate that, MKFF-CNN achieves an accuracy of 97.65% on the gForce dataset, which is 1.39% higher than that of the subnetwork without early fusion, and at least 6.54% higher than that of the subnetwork with single-scale convolution kernel. On the Ninapro DB6, MKFF-CNN achieves 98.52% accuracy, which is 1.3% higher than the state-of-the-art works, showing the superiority of the proposed model.
Recurrent 3D Hand Pose Estimation Using Cascaded Pose-Guided 3D Alignments

3D hand pose estimation is a challenging problem in computer vision due to the high degrees-of-freedom of hand articulated motion space and large viewpoint variation. As a consequence, similar poses observed from multiple views can be dramatically different. In order to deal with this issue, view-independent features are required to achieve state-of-the-art performance. In this paper, we investigate the impact of view-independent features on 3D hand pose estimation from a single depth image, and propose a novel recurrent neural network for 3D hand pose estimation, in which a cascaded 3D pose-guided alignment strategy is designed for view-independent feature extraction and a recurrent hand pose module is designed for modeling the dependencies among sequential aligned features for 3D hand pose estimation. In particular, our cascaded pose-guided 3D alignments are performed in 3D space in a coarse-to-fine fashion. First, hand joints are predicted and globally transformed into a canonical reference frame; Second, the palm of the hand is detected and aligned; Third, local transformations are applied to the fingers to refine the final predictions. The proposed recurrent hand pose module for aligned 3D representation can extract recurrent pose-aware features and iteratively refines the estimated hand pose. Our recurrent module could be utilized for both single-view estimation and sequence-based estimation with 3D hand pose tracking. Experiments show that our method improves the state-of-the-art by a large margin on popular benchmarks with the simple yet efficient alignment and network architectures.
AttrE2vec: Unsupervised attributed edge representation learning

Representation learning has overcome the often arduous and manual featurization of networks through (unsupervised) feature learning as it results in embeddings that can apply to a variety of downstream learning tasks. The focus of representation learning on graphs has focused mainly on shallow (node-centric) or deep (graph-based) learning approaches. While there have been approaches that work on homogeneous and heterogeneous networks with multi-typed nodes and edges, there is a gap in learning edge representations. This paper proposes a novel unsupervised inductive method called AttrE2Vec, which learns a low-dimensional vector representation for edges in attributed networks. It systematically captures the topological proximity, attributes affinity, and feature similarity of edges. Contrary to current advances in edge embedding research, our proposal extends the body of methods providing representations for edges, capturing graph attributes in an inductive and unsupervised manner. Experimental results show that, compared to contemporary approaches, our method builds more powerful edge vector representations, reflected by higher quality measures (AUC, accuracy) in downstream tasks as edge classification and edge clustering. It is also confirmed by analyzing low-dimensional embedding projections.
Active Anomaly Detection for Key Item Selection in Process Auditing

Abstract Process mining allows auditors to retrieve crucial information about transactions by analysing the process data of a client. We propose an approach that supports the identification of unusual or unexpected transactions, also referred to as exceptions. These exceptions can be selected by auditors as “key items”, meaning the auditors wants to look further into the underlying documentation of the transaction. The approach encodes the traces, assigns an anomaly score to each trace, and uses the domain knowledge of auditors to update the assigned anomaly scores through active anomaly detection. The approach is evaluated with three groups of auditors over three cycles. The results of the evaluation indicate that the approach has the potential to support the decision-making process of auditors. Although auditors still need to make a manual selection of key items, they are able to better substantiate this selection. As such, our research can be seen as a step forward with respect to the usage of anomaly detection and data analysis in process auditing.
Generalized Path Planning for UTM Systems With a Space-Time Graph

Motivated by the increased use of UAS in commercial applications, in this paper we tackle the problem of path planning when requests are submitted by UAS managed by different operators.We propose the new problem of generalized path planning for UAS Traffic Management, where the UAS path is described by operators with a sequence of waypoint groups and a solution trajectory must pass through a waypoint in each group.This problem is typical for applications where multiple charging stations and pickup/drop-off locations are distributed in a flight area.Our solution builds upon prior work on discretized space-time graph path planning and proposes a novel multi-source/multi-destination graph search algorithm that generates collision-free trajectories for pre-flight CDR.Our efficient algorithm has runtime proportional to the number of groups and avoids combinatorial explosion.We apply our mechanism to the energy-constrained UAS package delivery problem with multiple warehouses and battery charging stations.Simulation results show that our algorithm is efficient and scalable with the number of requests and graph size.The addition of charging stations and the option for multiple warehouses increases the request admission ratio and reduces the overall trajectory duration, effectively improving both the planner's quality of service and the efficiency of airspace usage.
Application of machine learning models to predict malaria using malaria cases and environmental risk factors

Malaria remains a significant cause of deaths and illness especially in sub-Saharan Africa (SSA). The efforts to eliminate malaria include the use of intermittent preventive prophylaxis (ITPp), indoor residual spraying (IRS), long-lasting insecticide-treated nets (LLINs), malaria prevention strategies and behavioural change education. Among these initiatives, predicting malaria cases at the ward level tremendously assist in malaria elimination, yet its application is still low. Therefore, this paper applied logistic regression, decision trees classifier, support vector machine, and random forest classifier to predict malaria in Buhera district. The study shows that logistic regression performs better, with 83% accuracy, 82% precision and 90% F1-score than other machine learning classifiers when predicting malaria outbreaks using environmental risk factors. These models can aid decision-makers to effectively allocate resources, development of malaria early warning systems, optimize the distribution of indoor residual spraying teams and spraying equipment, giving more priority to high sporadic areas.
Rethinking Click Embedding for Deep Interactive Image Segmentation

In interactive image segmentation methods, users can participate in and influence the segmentation process through their interactions, such as scribbles or bounding boxes. Similarly, the process of deep interactive segmentation utilizes users’ interactions to guide the network to learn the target of interest. This article mainly considers mouse clicking, which is the simplest interaction mode. Then, how to effectively characterize the click interaction (we call this “click encoding”) and fuse the click-related information with the network are the key issues in a deep interactive segmentation framework. However, the current click encoding method concentrates only on the spatial information of the clicks, so the region affected by each click is difficult to control, and the stability of the network is therefore reduced. Therefore, we propose a feature-interactive map that builds a close relationship between interaction information and target semantics. The affected region of the feature interactive map is determined by semantic information. Furthermore, we introduce an interactive nonlocal block by embedding a feature-interactive map into a nonlocal block, so that the long-range dependencies of the interaction information can be captured. Finally, based on the early fusion strategy, the features of the interactive nonlocal block are fused with the high-level features, thus amplifying the impacts of position and semantics on the final prediction results. Comprehensive experiments demonstrate that our click embedding approach significantly boosts the efficiency of the network and achieves state-of-the-art performance.
Implementing Durative Actions with Failure Detection in Gwendolen

We present an extension of the semantics for action execution in the Gwendolen BDI programming language. This extension firstly explicitly assumes that actions have durations and, moreover, that the reasoning cycle of the agent can not be stopped while such an action is executing but needs to continue in order to monitor for important external events. Secondly, the extension assumes that actions may often fail and this needs to be detected. This forms part of a larger project to develop a framework plan/action adaptation within BDI agents in order to enable long-term autonomy. We have implemented the extension and demonstrate its operation in a simple case study.
CoGS: Controllable Generation and Search from Sketch and Style

We present CoGS, a novel method for the style-conditioned, sketch-driven synthesis of images. CoGS enables exploration of diverse appearance possibilities for a given sketched object, enabling decoupled control over the structure and the appearance of the output. Coarse-grained control over object structure and appearance are enabled via an input sketch and an exemplar "style" conditioning image to a transformer-based sketch and style encoder to generate a discrete codebook representation. We map the codebook representation into a metric space, enabling fine-grained control over selection and interpolation between multiple synthesis options before generating the image via a vector quantized GAN (VQGAN) decoder. Our framework thereby unifies search and synthesis tasks, in that a sketch and style pair may be used to run an initial synthesis which may be refined via combination with similar results in a search corpus to produce an image more closely matching the user's intent. We show that our model, trained on the 125 object classes of our newly created Pseudosketches dataset, is capable of producing a diverse gamut of semantic content and appearance styles.