Reviewerly/exHarmony huggingface dataset:
Multiple Frequency Inputs and Context-Guided Attention Network for Stereo Disparity Estimation

Deep learning-based methods have made remarkable progress for stereo matching in terms of accuracy. However, two issues still hinder producing a perfect disparity map: (1) blurred boundaries and the discontinuous disparity of a continuous region on disparity estimation maps, and (2) a lack of effective means to restore resolution precisely. In this paper, we propose to utilize multiple frequency inputs and an attention mechanism to construct the deep stereo matching model. Specifically, high-frequency and low-frequency information of the input image together with the RGB image are fed into a feature extraction network with 2D convolutions. It is conducive to produce a distinct boundary and continuous disparity of the smooth region on disparity maps. To regularize the 4D cost volume for disparity regression, we propose a 3D context-guided attention module for stacked hourglass networks, where high-level cost volumes as context guide low-level features to obtain high-resolution yet precise feature maps. The proposed approach achieves competitive performance on SceneFlow and KITTI 2015 datasets.
Formalizing dynamic-wind in the lambda calculus

The Scheme programming language has the control constructs: call-with-current-continuation (call/cc) and dynamic-wind. The construct call/cc is a procedure to handle first-class continuations. The construct dynamic-wind is a procedure to hook a continuation captured by call/cc. It can add pre- and post-processing to a continuation, and allows flexible handling of global escape and control effect by continuation calls. The first example where the continuation is applied is rewriting variables with dynamic scope, and the second is saving and restoring registers during context switching of threads implemented in user space.
Robot motion planning with orientational constraints based on offline sampling datasets

Currently available algorithms developed for planning robot motion paths in configuration spaces with both obstacle avoidance constraints and end-effector pose constraints suffer from low sampling rates, long computation times, and requirements for specific manipulator structures. The present work addresses this issue by developing an improved sampling-type motion planning algorithm based on a modified form of the rapidly-exploring random tree (RRT) algorithm. The sampling process is based on the standard premise that the constrained manifold is continuous within a particular range. Therefore, configurations that meet the specific constraints associated with a proscribed motion planning task are sampled in advance, and those configurations meeting the constraints are stored in an offline configuration dataset that is employed exclusively in the RRT process. Accordingly, the proposed algorithm facilitates a greatly streamlined motion planning process for manipulators with high degrees of freedom. The computational speed and planning precision of the algorithm are further enhanced by introducing a target bias mechanism and applying an adaptive mechanism to improve the obstacle collision detection performance. The high motion-planning performance of the proposed algorithm is verified by comparisons with the performances of other state-of-the-art RRT-based algorithms based on numerical simulations.
Federated strong tracking filtering for nonlinear systems with multiple sensors

In this paper, the filtering problem is investigated for nonlinear systems with multiple sensors. A federated strong tracking filter is designed to track state mutations by making full use of limited sensor information. Several subsystems are composed of different sensor combinations, and their states are independently estimated by using local filters in parallel. The strong tracking filter, as local filters, adaptively adjusts gain matrices of filters by introducing fading factors to track state mutations timely. Based on the theory of boundedness and inequality technique, the fusion estimation error is proved to be exponentially bounded in mean square. Finally, the feasibility and effectiveness of the proposed method is demonstrated by an experiment concerning the rotary steering drilling tool system.
Constrained Image-Based Visual Servoing of Robot Manipulator with Third-Order Sliding-Mode Observer

A new image-based robot visual servo control strategy based on a third-order sliding-mode observer (TOSM) model predictive control is proposed in this study. This new control strategy solves the problem of robot visual servo control with system constraints and time-varying disturbances when the camera and model of the robot manipulator are uncertain and the joint velocity is unknown. In the proposed method, the joint velocity and system centralized uncertainties are estimated simultaneously based on a third-order sliding-mode observer, and the image-based visual servoing problem is transformed into a nonlinear optimization problem based on a model predictive control method considering both visibility constraints and actuator constraints, which minimizes the predicted trajectory cost function to generate the control signal for each cycle. Simulations were carried out to verify the effectiveness of the proposed control scheme.
Automated machine learning pipeline for geochemical analysis

Abstract Biplot diagrams are traditionally used for rock discrimination using geochemical data from samples. However, this approach has limitations when facing a high number of variables. Machine learning has been proposed as an alternative to analyze multivariate data for more than 70 years. However, the application of machine learning by geoscientists is still complicated since there are no tools that propose a pipeline that can be followed from preparing the data to evaluating the models. Automated machine learning aims to face this issue by automating the creation and evaluation of machine learning models. The contribution of this work is twofold. First, we propose a methodology that follows a pipeline for the application of supervised and unsupervised learning to geochemical data. Both methods were applied to a dataset of granitic rock samples from 6 blocks in the Peninsular Ranges and the Transverse Ranges Provinces in Southern California. For supervised learning, the Decision Trees model offered the best values to classify the samples from this region: accuracy: 87%; precision: 89%; recall: 89%; and F-score: 81%. For unsupervised learning, 2 components were related to pressure effects, and another 2 could be related to water effects. As a second contribution, we propose a web application that follows the proposed methodology to analyze geochemical data using automated machine learning. It allows data preparation using techniques such as imputation and upsampling, the application of supervised and unsupervised learning, and the evaluation of the models. All this without the need to program.
Tectonic setting, fluid inclusion and gold mineralization of the southwest Poli region (northern Cameroon Domain)

This paper documents original petrographic and structural data on metamorphic and magmatic rocks as well as the occurrence of gold mineralization in the Dourou Tchaga area (SW Poli) in the northwestern Cameroon domain. The study area is dominated by high-grade gneisses derived from a volcanic-sedimentary series alternating with orthogneiss and magmatic rocks. Four main deformation stages were distinguish comprising the early D1 and D2 deformations showing ductile behavior under amphibolite to granulite facies, followed by retrogression into greenschist facies. D3 and D4 are characterized by ductile-brittle and late brittle structures. Gold mineralization is hosted in laminated quartz veins in steeply dipping, ENE–WSW-trending, sinistral shear zone developed during D3. Host rocks are high-grade metamorphic paragneiss and orthogneiss. Ore minerals include native gold, galena, sphalerite, pyrite and chalcopyrite; wall rock alteration assemblage includes stable pyrite, magnetite and hematite. This assemblage together with specific quartz microstructures indicate gold mineralization at lower greenschist facies conditions. Gold is disseminated in quartz veins and wall rock alteration zones, where it occurs as free grains but also as inclusions in pyrite and sphalerite. Two types of fluid inclusions are distinguished: aqueous-carbonic CO2–H2O and aqueous (H2O), both with low to moderate (2.0–13.9 wt% NaCleq) salinity and variable CO2 content of 13–23 mole% for the aqueous-carbonic fluid inclusions. Fluid inclusions data and alteration assemblage provide evidence that gold precipitation in quartz veins occurred in response to an increase in fO2 caused by fluid pressure fluctuations in the shear zone. Gold mineralization in the northern Cameroon domain was formed during post-collisional stage and retrograde exhumation of the terrane in the greenschist facies toward the end of the Panafrican Orogeny.
Face Mask Detection using Artificial Intelligence for workplaces

During the COVID-19 pandemic, face masks detection has become a critical responsibility. Since the pandemic is still ongoing, and workplaces are opening up it is absolutely necessary that all COVID-19 rules & regulations issued by the government which require all employees in a workplace to wear a face mask to be followed. In order to enforce this rule with severity a face mask detection system can be set up in the workplace. This is achieved with the help of artificial intelligence models, face recognition based neural network and transfer learning approach. Once the model is developed it will be integrated with an application which will keep reminding the employee to wear a mask and also keep records and track of the times the employee was liable to pay a penalty.
A meta-evolutionary selection of constituents in ensemble differential evolution algorithm

A Meta-evolutionary Selection of Constituents in Ensemble DE (MeSCEDE) framework is proposed in this paper to automate the design of high-level multi-population ensemble Differential Evolution (DE) algorithms. The automated design of high-level multi-population ensemble DE algorithms involves both automated selection of constituent DE algorithms for the ensemble and automated configuration of ensemble related parameters. Grammatical Evolution has been used as the meta-evolutionary algorithm in MeSCEDE to search the space of design choices so as to evolve effective ensemble design(s) for given problem(s). The simulation experiments carried out in this paper involve applying MeSCEDE to evolve ensemble DE designs for solving 30-dimensional CEC’17 benchmark functions. The effectiveness of the evolved designs are validated on 30 and 50-dimensional CEC’14 functions as well as on 22 real-world problem instances from CEC’11 benchmark suite. The MeSCEDE evolved designs have exhibited a competitive performance against state-of-the-art ensemble DE algorithms. In addition, the potential of MeSCEDE has been demonstrated against irace , a state-of-the-art algorithm configurator. All simulation experiments reiterate the potential of MeSCEDE towards evolving effective and robust ensemble DE designs. • Automated design of ensemble differential evolution (DE) algorithm is presented. • The proposed approach is Meta-evolutionary Selection of Constituents in Ensemble DE. • The approach uses grammatical evolution as the meta evolutionary algorithm. • The approach is demonstrated on CEC’17, CEC’14 and CEC’11 benchmark suites. • Relieves the practitioners from the difficult task of algorithm configuration.
Multi-Headed Deep Learning Models to Detect Abnormality of Alzheimer’s Patients

Worldwide, many elders are suffering from Alzheimer's disease (AD).The elders with AD exhibit various abnormalities in their activities, such as sleep disturbances, wandering aimlessly, forgetting activities, etc., which are the strong signs and symptoms of AD progression.Recognizing these symptoms in advance could assist to a quicker diagnosis and treatment and to prevent the progression of Disease to the next stage.The proposed method aims to detect the behavioral abnormalities found in Daily activities of AD patients (ADP) using wearables.In the proposed work, a publicly available dataset collected using wearables is applied.Currently, no real-world data is available to illustrate the daily activities of ADP.Hence, the proposed method has synthesized the wearables data according to the abnormal activities of ADP.In the proposed work, multi-headed (MH) architectures such as MH Convolutional Neural Network-Long Short-Term Memory Network (CNN-LSTM), MH one-dimensional Convolutional Neural Network (1D-CNN) and MH two dimensional Convolutional Neural Network (2D-CNN) as well as conventional methods, namely CNN-LSTM, 1D-CNN, 2D-CNN have been implemented to model activity pattern.A multi-label prediction technique is applied to detect abnormal activities.The results obtained show that the proposed MH architectures achieve improved performance than the conventional methods.Moreover, the MH models for activity recognition perform better than the abnormality detection.
An elastic recommender process for cloud service recommendation scalability

Abstract Cloud computing services are ubiquitous in society and cloud recommender systems play a crucial role in intelligently selecting services for cloud users. Currently, recommendations are static with low scalability. Only one recommendation list is generated at a time and the recommender strategy in the recommendation cycle is not adjustable. This paper presents a new elastic recommender process (ERP) for cloud users. A Markov model is used to characterize the dynamic relationship between different user states. The ERP generates an elastic recommendation that can be used to dynamically adjust the recommender strategy to meet the user's needs based on their browsing records in the current service cycle without the recommender system's involvement. Experimental results show that the ERP improves the effectiveness of the recommender thus increasing the accuracy and diversity of its recommendations.
Mixed Reality Communication System for Procedural Tasks

We design a volumetric communication system for remote assistance of procedural medical tasks. The system allows a remote expert to spatially guide a local operator using a real-time volumetric representation of the patient. Guidance is provided by voice, virtual hand metaphor, and annotations performed in situ. We include the feedback we received from the medical professionals and early NASA TLX [5] data on the cognitive load of the system.
SFQ: Constructing and Querying a Succinct Representation of FASTQ Files

A large and ever increasing quantity of high throughput sequencing (HTS) data is stored in FASTQ files. Various methods for data compression are used to mitigate the storage and transmission costs, from the still prevalent general purpose Gzip to state-of-the-art specialized methods. However, all of the existing methods for FASTQ file compression require the decompression stage before the HTS data can be used. This is particularly costly with the random access to specific records in FASTQ files. We propose the sFASTQ format, a succinct representation of FASTQ files that can be used without decompression (i.e., the records can be retrieved and listed online), and that supports random access to individual records. The sFASTQ format can be searched on the disk, which eliminates the need for any additional memory resources. The searchable sFASTQ archive is of comparable size to the corresponding Gzip file. sFASTQ format outputs (interleaved) FASTQ records to the STDOUT stream. We provide SFQ, a software for the construction and usage of the sFASTQ format that supports variable length reads, pairing of records, and both lossless and lossy compression of quality scores.
Temporally Multi-Modal Semantic Reasoning with Spatial Language Constraints for Video Question Answering

Video question answering (QA) aims to understand the video scene and underlying plot by answering video questions. An algorithm that can competently cope with this task needs to be able to: (1) collect multi-modal information scattered in the video frame sequence while extracting, interpreting, and utilizing the potential semantic clues provided by each piece of modal information in the video, (2) integrate the multi-modal context of the above semantic clues and understand the cause and effect of the story as it evolves, and (3) identify and integrate those temporally adjacent or non-adjacent effective semantic clues implied in the above context information to provide reasonable and sufficient visual semantic information for the final question reasoning. In response to the above requirements, a novel temporally multi-modal semantic reasoning with spatial language constraints video QA solution is reported in this paper, which includes a significant feature extraction module used to extract multi-modal features according to a significant sampling strategy, a spatial language constraints module used to recognize and reason spatial dimensions in video frames under the guidance of questions, and a temporal language interaction module used to locate the temporal dimension semantic clues of the appearance features and motion features sequence. Specifically, for a question, the result processed by the spatial language constraints module is to obtain visual clues related to the question from a single image and filter out unwanted spatial information. Further, the temporal language interaction module symmetrically integrates visual clues of the appearance information and motion information scattered throughout the temporal dimensions, obtains the temporally adjacent or non-adjacent effective semantic clue, and filters out irrelevant or detrimental context information. The proposed video QA solution is validated on several video QA benchmarks. Comprehensive ablation experiments have confirmed that modeling the significant video information can improve QA ability. The spatial language constraints module and temporal language interaction module can better collect and summarize visual semantic clues.
Learning Robust Shape-Indexed Features for Facial Landmark Detection

In facial landmark detection, extracting shape-indexed features is widely applied in existing methods to impose shape constraint over landmarks. Commonly, these methods crop shape-indexed patches surrounding landmarks of a given initial shape. All landmarks are then detected jointly based on these patches, with shape constraint naturally embedded in the regressor. However, there are still two remaining challenges that cause the degradation of these methods. First, the initial shape may seriously deviate from the ground truth when presented with a large pose, resulting in considerable noise in the shape-indexed features. Second, extracting local patch features is vulnerable to occlusions due to missing facial context information under severe occlusion. To address the issues above, this paper proposes a facial landmark detection algorithm named Sparse-To-Dense Network (STDN). First, STDN employs a lightweight network to detect sparse facial landmarks and forms a reinitialized shape, which can efficiently improve the quality of cropped patches when presented with large poses. Then, a group-relational module is used to exploit the inherent geometric relations of the face, which further enhances the shape constraint against occlusion. Our method achieves 4.64% mean error with 1.97% failure rate on COFW68 dataset, 3.48% mean error with 0.43% failure rate on 300 W dataset and 7.12% mean error with 11.61% failure rate on Masked 300 W dataset. The results demonstrate that STDN achieves outstanding performance in comparison to state-of-the-art methods, especially on occlusion datasets.
Cross Task Modality Alignment Network for Sketch Face Recognition

The task of sketch face recognition refers to matching cross-modality facial images from sketch to photo, which is widely applied in the criminal investigation area. Existing works aim to bridge the cross-modality gap by inter-modality feature alignment approaches, however, the small sample problem has received much less attention, resulting in limited performance. In this paper, an effective Cross Task Modality Alignment Network (CTMAN) is proposed for sketch face recognition. To address the small sample problem, a meta learning training episode strategy is first introduced to mimic few-shot tasks. Based on the episode strategy, a two-stream network termed modality alignment embedding learning is used to capture more modality-specific and modality-sharable features, meanwhile, two cross task memory mechanisms are proposed to collect sufficient negative features to further improve the feature learning. Finally, a cross task modality alignment loss is proposed to capture modality-related information of cross task features for more effective training. Extensive experiments are conducted to validate the superiority of the CTMAN, which significantly outperforms state-of-the-art methods on the UoM-SGFSv2 set A, set B, CUFSF, and PRIP-VSGC dataset.
Dynamic Evaluation and Prediction for Macroeconomic Based on IGA-LSSVM

Among all statistics, government statistics plays an extremely important role in socioeconomic activities, while the continuous changes of external factors bring new challenges to government statistics and have a great impact on the quality of statistics. With the rapid development of the economy and the maturity of the market economy system, the demand for government statistics from all walks of life has become more and more vigorous. In order to effectively improve the accuracy of macroeconomic outflow forecasting and further secure economic production, a weighted least squares support vector machine (LS-SVM) optimized with the immune genetic algorithm (IGA) is proposed to build a macroeconomic outflow forecasting model. For the nonlinearity, time-varying, and complexity of the economic outflow system, a new weighted strategy function is proposed to improve the LS-SVM, and then the IGA is introduced to optimize the improved LS-SVM with the kernel parameter δ and the regularization parameter γ. Finally, an experimental analysis is conducted using economic emergence data from economic history. The results show that the maximum relative error of forecasting using the model is 2.763%, the minimum relative error is 0.705%, the average relative error is 1.3298%, and the model has faster convergence, stronger generalization ability, and higher forecasting accuracy than other forecasting models.
"Why would Someone Hack Me out of Thousands of Students": Video Presenter's Impact on Motivating Users to Adopt 2FA

The voluntary adoption rate of two-factor authentication (2FA) remains low. This paper investigates whether video-based risk communication messages about Duo 2FA impacts voluntary 2FA adoption rate when delivered by a human speaker versus a cartoon speaker. We conducted an online two-phased survey-based study with 435 university students comprised of those who have not enabled Duo 2FA (non-adopters) on their university account as well as those who had previously enabled Duo 2FA (adopters). Participants in the non-adopters group (139) were assigned to one of the three groups: Threat-R (human speaker video), Threat-A (cartoon speaker video), and Control (no video). We found that 31% of participants enabled Duo 2FA through the human speaker video message compared to 7% with the cartoon speaker video message. However, there was no significant difference between the treatment and control groups (17% of participants enabled Duo 2FA in the Control group). Nevertheless, the treatment group participants showed their intention to activate Duo 2FA on their university account in the future. Those who enabled Duo 2FA rated Duo's usability as good. Moreover, enabling Duo 2FA on university accounts led some participants to enable 2FA on other online accounts. Our findings suggest that risk communication through videos that have a human speaker could increase users' willingness to adopt security features.