Reviewerly/exHarmony huggingface dataset:
Function Analysis of Web Services Based on REST Protocol with Selected Frameworks

Nowadays, one of the most common developing web services models is REST (Representational State Transfer). REST model provides a set of defined rules specifying how resources and the methods used to access them are presented. Therefore this architecture is supported by multiple programming languages. On the other hand end-customers or front-end developers do not mind the technology for implementation of created website or mobile application. Therefore there is a big need to provide functional and efficient interface that will fulfil the customer needs. The paper presents the analysis of technology performance based on the research concerning the comparison of web services operation based on REST protocol using NodeJS and Spring Boot frameworks. Two web services were implemented using NodeJS and Spring Boot containing several standard computational and database operations.
Towards a Masked Face Recognition Algorithm: A Novel Rule Based Hybrid Algorithm

The recent SARS-CoV-2 virus global spread and the Covid-19 pandemic that resulted from that has increased the focus on hygienic and contactless safety measures. The wide use of masks, that are essential for the reduction of the spread of the virus, has been declared mandatory by several institutions and countries. Given the circumstances, masked facial recognition has gained an increased attention by the research community. Mask identification for healthcare reasons, face recognition and identification for public safety in a smart city environment and security reasons proved to be a challenging problem with most algorithms having limitations. The vast amount of data, of people with masks, that has been generated in the recent months will be helpful in tackling some of the major issues. The main issues most methodologies face are occlusion, illumination, non-frontal characteristics and pose variation. This paper focuses on the preliminary results of a novel rule based hybrid masked face recognition algorithm. We use the MaskedFace-net dataset and we detect the covered face using the Viola-Jones algorithm. Through the use of Statistical Region Merging (SRM) we detect the ocular region in the cropped face image, and we achieve eye detection for our preprocessing. Through this process we manage to get a clearer and sharper version of the original input eye image. Finally, we generate the attributed graph of the detected facial features, whose labeled arcs represent the computed distance rations between them. This algorithm will act as input into a machine learning prediction model moving forward.
A comparative study of validity indices on estimating the optimal number of clusters

In clustering, finding the optimal number of clusters is usually one of the most crucial steps in the whole partitioning process. The decision about the optimal number of clusters, however, is not easy to make. In addition, the term ”optimal” is rather vague. In general, determining the optimal number of clusters is directly dependent on the method used to measure similarities and the parameter selection of the partition method. Moreover, certain inherent characteristics of the datasets, such as clusters that overlap with each other or clusters that contain subclusters, may, most often, increase the task’s level of difficulty. Given the above, in order to tackle the problem of estimating such an optimal in each distinct clustering case, different kind of indicators have over the years been proposed. In this study, a large number of such indicators, called validity indices, based on the approach of the so-called relative criteria, are examined comparatively. Specifically, a total of 26 validity indices are examined in two separate study cases: one in real-world and one in artificially generated data. Every index is utilized under the schemes of 9 different clustering methods which incorporate a total of 5 different distance metrics. The results are presented in various explanatory forms.
SynSLaG: Synthetic Sign Language Generator

Machine learning techniques have the potential to play an important role in sign language recognition. However, sign language datasets lack the volume and variety necessary to work well. To enlarge these datasets, we introduce SynSLaG, a tool that synthetically generates sign language datasets from 3D motion capture data. SynSLaG generates realistic images of various body shapes with ground truth 2D/3D poses, depth maps, body-part segmentations, optical flows, and surface normals. The large synthetic datasets provide possibilities for advancing sign language recognition and analysis.
Caps-LSTM: A Novel Hierarchical Encrypted VPN Network Traffic Identification Using CapsNet and LSTM

At present, encryption technologies are widely applied in the network, providing a lot of opportunities for attackers to hide their command and control activities, and thus encrypted traffic detection technology is one of the important means to prevent malicious attacks in advance. The existing methods based on machine learning cannot get rid of the artificial dependence of feature selection. Moreover, deep learning methods ignore the hierarchical characteristics of traffic. Therefore, we propose a novel deep neural network that combines CapsNet and LSTM to implement a hierarchical encrypted traffic recognition model, Caps-LSTM, which splits the traffic twice and classifies the encrypted traffic hierarchically based on the temporal and spatial characteristics, where CapsNet learns the lower spatial characteristics of the traffic and LSTM learns the upper temporal characteristics of the traffic. Finally, the softmax classifier is used to achieve effective detection of encrypted traffic services and specific application categories. Compared with the existing advanced methods based on the common data set ISCX VPN-nonVPN, the experimental results show that Caps-LSTM is more effective.
Enhanced Few-Shot Learning with Multiple-Pattern-Exploiting Training

The NLPCC 2021 Few-shot Learning for Chinese Language Understanding Evaluation (FewCLUE) shared task seeks for the best solution to few-shot learning tasks with pre-trained language models. This paper presents Tencent Cloud Xiaowei's approach to this challenge, which won the 2st place in the contest. We propose a Multiple-Pattern-Exploiting Training method (MPET) for the challenge. Different from the original PET, MPET constructs multiple patterns to enhance the model's generalization capability. We take the MPET as an auxiliary task, and jointly optimize classification and MPET. Empirical results show that our MPET is effective to few-shot learning tasks.
Circulatory interfaces: Perpetuating power through practices, content, and positionality

As circulation is a type of writing that functions as a habit of citizenship, it has important ethical implications for the power element of social justice. However, the act of circulation cannot be separated from the interfaces in which it is embedded, nor can it be separated from the interfaces which circulate, as those interfaces also perpetuate and produce power dynamics. In this article, I explore the intersection of interfaces and circulation through two computer interfaces and one printed interface. Ultimately, interfaces can create normative circulatory (1) practices, (2) content, and (3) positions. They produce norms about who circulates what information and how people circulate that information. In particular, I examine the Coronavirus Communication Toolkit and the Interactive Coronavirus Prevention Flyer Builder on the U.S. Chamber of Commerce's website as they created norms for circulating pre-existing content, obscured culpability, brought users into mutually transformative ideological assemblages, and maintained power relationships as secondary interfaces circulated and interpellated individuals. I end with heuristic questions for designers and suggestions for future research.
Generation of Personalized Knowledge Graphs Based on GCN

Education must follow the principle of teaching students in accordance with their aptitude.In this paper, we propose a novel method to generate personalized knowledge graphs based on graph convolutional network.We have summarized the methods of evaluating the difficulty of exercises, and apply them to the generation of knowledge graph.After that, the adjacency matrix corresponding to the knowledge graph and the eigenvectors corresponding to the nodes are used as inputs of the graph convolutional network, and the semi-supervised leaning node classification is adopted to continuously iterate the training to optimize the graph convolution neural network model.Meanwhile, the graph convolutional neural network is used to generate personalized knowledge graph for each student, more accurate personalized services can be provided.The experimental results show that our method can make a better to realize in-depth personalized services.
Learning Disentangled Factors from Paired Data in Cross-Modal Retrieval: An Implicit Identifiable VAE Approach

We tackle the problem of learning the underlying disentangled latent factors that are shared between the paired bi-modal data in cross-modal retrieval. Typically the data in both modalities are complex, structured, and high dimensional (e.g., image and text), for which the conventional deep auto-encoding latent variable models such as the Variational Autoencoder (VAE) often suffer from difficulty of accurate decoder training or realistic synthesis. In this paper we propose a novel idea of the implicit decoder, which completely removes the ambient data decoding module from a latent variable model, via implicit encoder inversion that is achieved by Jacobian regularization of the low-dimensional embedding function. Motivated from the recent Identifiable-VAE (IVAE) model, we modify it to incorporate the query modality data as conditioning auxiliary input, which allows us to prove that the true parameters of the model can be identifiable under some regularity conditions. Tested on various datasets where the true factors are fully/partially available, our model is shown to identify the factors accurately, significantly outperforming conventional latent variable models.
The effect of filtration size on the geochemistry of groundwater samples from a massive sulfide deposit at the Bathurst Mining Camp, New Brunswick, Canada

In hydrogeochemical studies, samples are commonly filtered to limit the fraction of analyte that is adsorbed or structurally bound to suspended particles, ensuring that only the dissolved fraction is analyzed, and thereby reducing analytical bias during measurement. The standard filter size that has been adopted is 0.45 μm, however, ultrafiltration can be used to remove colloidal particles two orders of magnitude smaller. In the following, we investigate the effect that standard (0.45 μm) and ultrafiltration (0.004 μm) have on the hydrogeochemistry of groundwaters from a volcanogenic massive sulfide (VMS) deposit at the Bathurst Mining Camp, New Brunswick, Canada. Groundwater samples were collected from six monitoring wells at the Nigadoo Mine tailings facility, and major and trace geochemistry were determined using a combination of inductively coupled plasma optical emission spectrometry (ICP-OES), inductively coupled plasma mass spectrometry (ICP-MS), and ion chromatography. Waters at the Nigadoo deposit are generally enriched in Ca and SO 4 , relative to other major cations and anions. Some element contents - including those associated with VMS deposits - differ depending on the filtration technique used (e.g., As, Fe, Pb, rare earth elements and yttrium [REY]), some are equally affected by both techniques (e.g., Cu, Ni, Zn), and some are unaffected by filtration (e.g., Ba, Ca, Mn, Cl - ). Shale-normalized REY anomalies (Ce SN /Ce SN *, Eu SN /Eu SN *, and Y SN /Ho SN ) and overall patterns can differ greatly (e.g., changing the sign of the anomaly) depending on the filtration technique used. We observe previously undocumented, and, at this time, unexplainable fractionation of Ho and Yb (non-redox sensitive REYs, unaffected by the tetrad effect) in unfiltered waters from the Nigadoo deposit. Differences in groundwater geochemistry induced by filtration technique can result in false positive and negative anomalies during environmental and exploration projects and must therefore be carefully considered. At the Nigadoo site, oxidation of sulfide minerals can occur, resulting in the formation of relatively unstable oxide minerals. Away from the tailings, where carbonate minerals are scarce and can no longer act as a pH buffer, the unstable oxide minerals break down and release metals and metalloids into the surrounding environment. The filtration methods used in this study can provide insight into where the specific metals and metalloids are hosted and how they are likely to behave under different redox conditions. Because VMS deposit pathfinder elements are enriched in unfiltered water, and differ by degree of filtration, geochemical analysis of the filtride material may also make an effective exploration tool. Thematic collection: This article is part of the Hydrochemistry related to exploration and environmental issues collection available at: https://www.lyellcollection.org/cc/hydrochemistry-related-to-exploration-and-environmental-issues
Phenomenology of writing with unfamiliar tools in a semi-public environment: A case study

• Graduate student writers have trouble adjusting to unfamiliar tools, hardware, and/or software. • Graduate students experience surveillance anxiety in a semi-public writing environment. • Research on student writing with unfamiliar digital tools and in semi-public environments is increasingly necessary. • More phenomenological writing research in similar contexts may focus our awareness on beneficial adjustments for writers. This case study investigates the phenomenon of graduate students writing with unfamiliar digital tools in semi-public writing environments. The increase in the prevalence of writing with unfamiliar tools in semi-public environments, such as networked computer classrooms and university testing centers, makes it worthy of investigation. We use phenomenological interviews to examine the writing experiences of a group of graduate students writing in a classroom on unfamiliar computers equipped with a tool that tracked their keystrokes and eye movements. Though some of the writers had positive experiences with the tool's output and their reflective conversations about writing it prompted, they all had challenging experiences adjusting to the hardware, the physical requirements of the tool, and overcoming surveillance anxiety prompted by it. Some students who wrote in the semi-public environment using an unfamiliar tool benefitted but all were challenged by situational awareness, new hardware, new haptic interactions, surveillance anxiety, and lack of control. The study indicates a need to explore and address the factors of situation awareness, an adjustment period, and surveillance anxiety in situations where individuals are writing with unfamiliar tools in semi-public environments.
Hand character gesture recognition based on a single millimetre‐wave radar chip

In this work, a new method for hand character gesture recognition based on a single millimetre-wave radar chip is proposed. Most current radar-based gesture recognition approaches use Doppler spectrograms or the feature extraction of Doppler spectrograms. In contrast, the authors propose using the trajectory points of gesture movements. Compared with the previous methods, the variations in the gesture movement speed, distance and direction during data acquisition have relatively little impact on the generated trajectories. The character trajectories drawn by gestures in the air output a fixed shape, and this fixed output facilitates the classification of gestures at a later stage. In this work, a single millimetre-wave radar is used to collect data and a three-dimensional fast Fourier Transform (3D-FFT) algorithm is used to obtain the gesture trajectories. For the trajectory points, a trajectory conversion image method is proposed and finally the improved Xception network proposed in this work is used for image recognition. Compared with traditional methods, the proposed method does not require large-scale data acquisition to facilitate different users. This work is concluded by recognising hand character gestures and comparing them with other commonly used classification methods to verify the gesture recognition accuracy of the proposed method.
Unified Algorithm Framework for Nonconvex Stochastic Optimization in Deep Neural Networks

This paper presents a unified algorithmic framework for nonconvex stochastic optimization, which is needed to train deep neural networks. The unified algorithm includes the existing adaptive-learning-rate optimization algorithms, such as Adaptive Moment Estimation (Adam), Adaptive Mean Square Gradient (AMSGrad), Adam with weighted gradient and dynamic bound of learning rate (GWDC), AMSGrad with weighted gradient and dynamic bound of learning rate (AMSGWDC), and Adapting stepsizes by the belief in observed gradients (AdaBelief). The paper also gives convergence analyses of the unified algorithm for constant and diminishing learning rates. When using a constant learning rate, the algorithm can approximate a stationary point of a nonconvex stochastic optimization problem. When using a diminishing rate, it converges to a stationary point of the problem. Hence, the analyses lead to the finding that the existing adaptive-learning-rate optimization algorithms can be applied to nonconvex stochastic optimization in deep neural networks in theory. Additionally, this paper provides numerical results showing that the unified algorithm can train deep neural networks in practice. Moreover, it provides numerical comparisons for unconstrained minimization using benchmark functions of the unified algorithm with certain heuristic intelligent optimization algorithms. The numerical comparisons show that a teaching-learning-based optimization algorithm and the unified algorithm perform well.
PathOS+: A New Realm in Expert Evaluation

Expert evaluation is commonly employed in usability research as it is fast and cost-effective. However, as it heavily relies on evaluators’ expertise, it is associated with problems of subjective interpretation. This is particularly noticeable in the games user research (GUR) field as games often aim to reach broad audiences and complex experiences in comparison to other applications. One way to improve the problems associated with subjective interpretation is to augment the evaluation with data collected from players. However collecting data from players can be time-consuming, expensive, and challenging to construct representative player samples. Building on our previous research on automated agent-based playtesting, in this work-in-progress paper we detail our new project in supplementing expert evaluation with simulated player data generated by a configurable population of artificial intelligence (AI) players.
Common Regions of Interest Extraction Based on Saliency Statistic Analysis for Multiple Remote Sensing Images

Various landscape characteristics and irregular object boundaries often make object extraction more difficult. Automated analysis of remote sensing (RS) images is challenging and saliency detection is an effective solution. Yet, many traditional algorithms emphasize simply on a single image and would, therefore, neglect the similarity of an image set. In this paper, concerning the relationships among images, a region of interest extraction model based on common features analysis for remote sensing images is proposed. Firstly, multi-image saliency maps, showing the common salient objects, are generated by clustering in RGB and CIELab color spaces. Next, a method, highlighting the salient region, is based on global and local saliency statistics analysis. Finally, regions of interest are segmented from original images according to saliency maps which have been made boundaries holding by superpixels. Experimental evaluation shows that compared with six existing models, we get more accurate saliency maps.
Artificially Intelligent Cyber Security: Reducing Risk and Complexity

Historically, research shows analysis, characterization, and classification of complex heterogeneous non-linear systems; and interactions have been difficult to accurately understand and effectively model. Synonymously, exponential growth of Internet of Things (IoT), cyber physical systems, and the litter of current accidental and unscrupulous cyber events portray an ever-challenging security environment wrought with complexity, ambiguity, and non-linearity, thus providing significant incentive to industry and academia toward advanced, predictive solutions to reduce persistent global threats. Recent advances in artificial intelligence (AI) and information theoretic methods (ITM) are benefitting disciplines struggling with learning from rapidly increasing data volume, velocity, and complexity. Research shows axiomatic design (AD) providing design and datum disambiguation for complex systems utilizing information content reduction. Therefore, we propose a transdisciplinary AD, AI/ML, ITM approach combining axiomatic design with advanced, novel, and adaptive machine-based learning techniques. We show how to significantly reduce risks and complexity by improving cyber system adaptiveness, enhancing cyber system learning, and increasing cyber system prediction and insight potential where today context is sorely lacking. We provide an approach for deeper contextual understanding of disjointed cyber events by improving knowledge density (KD) (how much we know about a given event) and knowledge fidelity (KF) (how well do we know) ultimately improving decision mitigation quality and autonomy. We improve classification and understanding of cyber data and reduce system non-linearity and cyber threat risk, thereby increasing efficiency by reducing labor and system costs, and “peace of mind.”
Machine Learning Based Improved Recommendation Model for E-learning

E-learning has gained importance due to the need of re-skilling, up-skilling, augmenting normal education system by providing knowledge delivery in virtual environment. A good E-learning system needs to have a customizable process, initiated by learner profile and dependent on training requirements. It can deliver desired results when it is integrated into day-to-day learning patterns providing a clear competitive edge for e-learning platforms. Learning needs to be relevant to the context of the concept. Learners in any learning environment differ in their learning style, level of knowledge, preferences, and attempts in solving and addressing problems when their expectations are not met. The current study illustrates and discusses a framework how machine learning (ML) technologies can be applied to e-learning systems to help the learner in selecting an appropriate learning course. Courses that require special privileges to be accessed can be handled according to the learner profile and learner's categories. In this paper, we present a comprehensive survey of current e-learning systems. Further an intelligent e-learning framework has been presented. The authors have applied 2 machine learning methods to the proposed framework and outcomes are discussed.