Reviewerly/exHarmony huggingface dataset:
Using Annotation Projection for Semantic Role Labeling of Low-Resourced Language: Sinhala

We present SinSRL, the first-ever semantic role labeller (SRL) for Sinhala, an Indo-European language spoken mainly in Sri Lanka. SinSRL takes parallel text in English (or any other language for which a suitable SRL exists) and Sinhala and outputs semantically annotated Sinhala text. We have enhanced existing tools to address several issues related to the target language. This will also be useful for labeling other Indic languages. In addition, we have manually semantically labeled a small Sinhala-English parallel dataset. The accuracy of our system is similar to that of manually labeled data. Our implementation can be used to generate a SRL dataset which may be used to train a direct semantic role labeller. SinSRL may be easily modified to annotate other low-resource languages for which parallel corpora are available.
Two-factor, continuous authentication framework for multi-site large enterprises

Security incidents originated from company employees or insiders give almost half of the attacks against organizations. Moreover, due to the pandemic situation, security mechanisms have a much higher role than ever. This paper proposes a two-factor authentication for multi-site large enterprises where continuous authentication, processing, and storage scalability and reliability are of primary importance. According to the requirements mentioned above, our solution is based on the Apache Spark framework extended with the Apache Cassandra, Kafka, and a MySQL database to provide data parallelism, fault tolerance, and distributed data storage.
Determining Real-Time Patterns of Lightning Strikes from Sensor Observations

Abstract Transient spatiotemporal events occur within a short interval of time, in a particular location. If such events occur unexpectedly with varying durations, frequencies, and intensities, they pose a challenge for near-real-time monitoring. Lightning strikes are examples of such events and they can have severe negative consequences, such as fires, or they precede sudden flash storms, which can result in damage to infrastructure, loss of Internet connectivity, interruption of electrical power supply, and loss of life or property. Furthermore, they are unexpected, momentary in occurrence, sometimes with high frequency and then again with long intervals between them, their intensity varies considerably, and they are difficult to trace once they have occurred. Despite their unpredictable and irregular nature, timely analysis of lightning events is crucial for understanding their patterns and behaviour so that any adverse effects can be mitigated. However, near-real-time monitoring of unexpected and irregular transient events presents technical challenges for their analysis and visualisation. This paper demonstrates an approach for overcoming some of the challenges by clustering and visualising data streams with information about lightning events during thunderstorms, in real time. The contribution is twofold. Firstly, we detect clusters in dynamic spatiotemporal lightning events based on space, time, and attributes, using graph theory, that is adaptive and does not prescribe number and size of clusters beforehand, and allows for use of multiple clustering criteria and thresholds, and formation of different cluster shapes. Secondly, we demonstrate how the space time cube can be used to visualise unexpected and irregular transient events. Along with the visualisation, we identify the interactive elements required to counter challenges related to visualising unexpected and irregular transient events through space time cubes.
Semi-Supervised Causal Inference for Identifying Pathogenic Social Media Accounts

The lack of sufficient labeled examples for devising and training sophisticated approaches to combat PSM accounts is still one of the foremost challenges facing social media firms. In contrast, unlabeled data is abundant and cheap to obtain thanks to the massive user-generated data produced on a daily basis. This chapter proposes a semi-supervised causal inference PSM detection framework, SemiPsm, to compensate for the lack of labeled data for identifying PSM users. The proposed method leverages unlabeled data in the form of manifold regularization and only relies on cascade information from users’ activities. This is in contrast to the existing approaches that use exhaustive feature engineering (e.g., profile information, network structure, etc.). Evidence from empirical experiments on the ISIS-B dataset from previous chapters suggests promising results of utilizing unlabeled instances for detecting PSMs.
The “Digital Facilitator”: An Extended Profile to Manage the Digital Transformation of Swiss Vocational Schools

Abstract Digital transformation requires the field of education to increasingly deal with the interplay between the teachers’ individual development of digital competence and institutional integration and adoption of digital technology. This is especially crucial in the vocational education context, which is closer to the world of work and, therefore, more sensitive to the disruptive changes of digitalisation. This chapter addresses this issue by proposing the result of a project that aimed to identify a new professional profile in the context of dual vocational education in Switzerland. The Digital Facilitator is a new term that refers to a teacher with advanced digital competency who is an expert in facilitating digital innovation adoption within educational institutions. Thus, it constitutes a possible concrete way to address the issue of integrating the individual and the organisational perspectives related to digital transformation. The profile is discussed by presenting an analysis of acknowledged existing frameworks and by considering the specificities of the context in which it was developed. The opportunities and challenges associated with introducing the profile are also critically addressed.
RETRACTED: Research and analysis of news transmission path based on microprocessor and image capture

The process of News and analysis theory recipient mind running. However, theoretical research in Finland in recent years provided some new ideas for this process. According to these ideas, all the effects and consequences of media information should be considered the process of knowledge formation when a person obtains information about his world view function, which is by two opposing principles as a guiding rule system dynamic sampling using information previously obtained from a sample selected for each new model. Therefore, the emotional piece has a greater potential fidelity, but at a more complex cost. Current strategies for dynamic non-uniform testing of images depend on examining areas in the most important variants. At the same time, ongoing advances in the hypothesis of the ideal exploration plan provide an ot homogeneous system for excellent testing based on the previous model's use. - Enormous fluffy control applications with virtual frameworks require continuous functionality to fast interface limits; High thickness programmable rationale gadgets, for example, can use the Field Programmable Gate Array (FPGA) to incorporate the most sensible in solitude. Picture transmission device and strategy using the CDMA Correspondence Network have been unveiled. Mechanical assembly to communicate the image captured by the camera.
A Generalized Coefficient of Determination for Mixtures of Regressions

One of the challenges in cluster analysis is the evaluation of the obtained clustering results without using auxiliary information. To this end, a common approach is to use internal validity criteria. For mixtures of linear regressions whose parameters are estimated via the maximum likelihood approach, we propose a three-term decomposition of the total sum of squares as a starting point to define some internal validity criteria. Exploiting this decomposition, local and overall coefficients of determination are, respectively, defined to judge how well the model fits the data group-by-group but also taken as a whole. An application to real data illustrates the use and the usefulness of these proposals.
On the Feasibility of Byzantine Agreement to Secure Fog/Edge Data Management

Fog/Edge computing improves the latency and security of data by keeping storage and computation close to the data source. Nevertheless, this raises other security challenges against malicious, a.k.a, Byzantine, attacks that can exploit the isolation of nodes, or when access to distributed data is required in untrusted environments. In this work, we study the feasibility of deploying Byzantine Agreement protocols to improve the security of fog/edge systems in untrusted environments. In particular, we explore existing Byzantine Agreement protocols, heavily developed in the Blockchain area, emphasizing the Consistency, Availability, and Partition-Tolerance tradeoffs in a geo-replicated system. Our work identifies and discusses three different approaches that follow the Strong Consistency, Eventual Consistency, and Strong Eventual Consistency models. Our conclusions show that Byzantine Agreement protocols are still immature to be used by fog/edge computing in untrusted environment due to their high finality latency; however, they are promising candidates that encourage further research in this direction.
Review and Comparison of Different Multi-Channel Spatial-Phase Shift Algorithms of Electronic Speckle Pattern Interferometry

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Electronic Speckle Pattern Interferometry (ESPI) is the most sensitive and accurate method for 3D deformation measurement in micro and sub micro-level. ESPI measures deformation by evaluating the phase difference of two recorded speckle interferograms under different loading conditions. By a spatial phase shift technique, ESPI allows for the rapid, accurate and continuous 3D deformation measurement. Multi-channel and carrier frequency are the two main methods of spatial phase shift. Compared with carrier frequency method, which is subject to the problem of spectrum aliasing, multi-channel method is more flexible in use. For extracting the phase value of speckles, four-step algorithm and five-step arbitrary phase algorithm are commonly used. Different algorithms have different spatial resolution, operational requirements, and phase image quality. In order to better understand the impact of different algorithms, we present a comparison of these methods of spatial phase measurement in terms of quality and operability. Besides, this paper also conducts experimental research and discussion on the relationship between speckle and corresponding pixels. Basic theory, brief derivation and optical layouts for the three algorithms will be presented. The potentials and limitations of the multi-channel spatial phase shift ESPI system will be demonstrated by examples of precise measurement of deformation.&lt;/div&gt;&lt;/div&gt;
On parameter estimation of the hidden Gaussian process in perturbed SDE.

We present results on parameter estimation of the linear partially observed Gaussian system of stochastic differential equations.We propose new one-step estimators which have the same asymptotic properties as the MLE, but much more simple to calculate, the estimators are so-called "estimator-processes".The construction of the estimators is based on the equations of Kalman-Bucy filtration and the asymptotic corresponds to the small noises in the observations and state (hidden process) equations.We give conditions which provide the consistency, asymptotic normality and asymptotic efficiency of the proposed estimators.
Rethinking Adaptive Computing: Building a Unified Model Complexity-Reduction Framework With Adversarial Robustness

Adaptive computing (AC) is a technique to dynamically select the layers to pass in a prespecified deep neural network (DNN) according to the input samples. In previous literature, AC was deemed as a standalone complexity-reduction skill. This brief studies AC through a different lens: we investigate how this strategy interacts with mainstream compression techniques in a unified complexity-reduction framework and whether its "input sample related" feature helps with the improvement of model robustness. Following this direction, we first propose a defensive accelerating branch (DAB) based on the AC strategy that can reduce the average computational cost and inference time of DNNs with higher accuracy compared with its counterparts. Then, the proposed DAB is jointly applied with the mainstream parameterwise compression skills, pruning and quantization, to build a unified complexity-reduction framework. Extensive experiments are conducted, and the results reveal quasi-orthogonality between the input-related and parameterwise complexity-reduction skills, which means that the proposed AC can be integrated into an off-the-shelf compressed model without hurting its accuracy. Besides, the robustness of the proposed compression framework is explored, and the experimental results demonstrate that DAB can be used as both the detector and the defensive tool when the model is under adversarial attacks. All these findings shed light on the great potential of DAB in building a unified complexity-reduction framework with both a high compression ratio and great adversarial robustness.
Permutations polynomials of the form G(X)k − L(X) and curves over finite fields

For a positive integer k and a linearized polynomial L(X), polynomials of the form $P(X)=G(X)^{k}-L(X) \in {\mathbb F}_{q^{n}}[X]$ are investigated. It is shown that when L has a non-trivial kernel and G is a permutation of $\mathbb {F}_{q^{n}}$ , then P(X) cannot be a permutation if $\gcd (k,q^{n}-1)>1$ . Further, necessary conditions for P(X) to be a permutation of $\mathbb {F}_{q^{n}}$ are given for the case that G(X) is an arbitrary linearized polynomial. The method uses plane curves, which are obtained via the multiplicative and the additive structure of $\mathbb {F}_{q^{n}}$ , and their number of rational affine points.
Partition-based feature screening for categorical data via RKHS embeddings

This paper proposes a new screening procedure for the ultrahigh dimensional data with a categorical response. By exploiting the group structure among predictors, a new partition-based screening approach is developed via the reproducing kernel Hilbert space (RKHS) embeddings in the maximum mean discrepancy framework. Consequently, the new method is able to identify the influential group of predictors that may be overlooked by the marginal screening methods. Moreover, by using the RKHS embedding, the new ranking index has a very simple form, and thus can be evaluated easily. As a by-product, the new method is model-free without specifying any relationship between the predictors and the response. The sure screening property of the proposed method is proved and the effectiveness of the new method is also illustrated via numerical studies and a real data analysis.
IOT Based Remote Monitoring for HT Motors in Thermal Power Plant

Abstract This paper explains about IOT Based remote monitoring for HT motor. HT motor were normally used widely in the industrial field in which it works on 3.3kv,6.6kv and in11kv. If any fault occurs, that causes HT motor to be failure. Which it leads to an shut down of generating station or in industrial field and there will be an chance of occurrence of heavy loss. To avoid such a failure, an continuous monitor had to be done. The main motto of this paper is to continuous monitoring of various parameters of motor like speed, vibration, temperature, current and voltage of HT motor and controls it by using an IOT.
Emotion Embedded Pose Generation

Body poses are a rich source of information in the field of sentiment analysis and they complement existing facial emotion recognition tasks by adding significant value especially when faces are not easily available. CCTV recordings and other non-human interfaces are applications where capturing facial expression is challenging and these interfaces can benefit from body pose emotion recognition to conduct context analysis. Another roadblock in this direction is the limited availability of collected and curated datasets of body poses with emotion labels. Addressing these issues, we propose two end-to-end pipelines to generate emotion conditioned human poses corresponding to specific emotion labels. An auxiliary conditional GAN network is presented for pose images and pose skeleton pipelines. The generated images improved emotion classification accuracy by an average of 5.40% across 4 different networks compared to images that were traditionally augmented. Additionally, through image and skeletal augmentation, we achieve state-of-the-art emotion classification results for the BEAST dataset.
Novel Joint Object Detection Algorithm Using Cascading Parallel Detectors

Object detection is an essential computer vision task that aims to detect target objects from an image. The traditional models are insufficient to generate a high-quality anchor box. To solve the problem, we propose a novel joint model called guided anchoring Region proposal networks and Cascading Grid Region Convolutional Neural Networks (RCGrid R-CNN), enhancing the ability of object detection. Our proposed model design is a joint object detection algorithm containing an anchor-based and an anchor-free branch in parallel and symmetry. In the anchor-based, we use nine-point spatial information fusion to obtain better anchor box location and introduce the shape prediction method of Guided Anchoring Region Proposal Networks (GA-RPN) to enhance the accuracy of the predicted anchor box. In the anchor-free branch, we introduce the Feature Selective Anchor-Free module (FSAF) to reduce the overlapping anchor boxes to obtain a more accurate anchor box. Furthermore, inspired by cascading theory, we cascade the new-designed detectors to improve the ability of object detection by setting a gradually increasing Intersection over Union (IoU) threshold. Compared with typical baseline models, we comprehensively evaluated our model by conducting experiments on two open datasets: Pascal VOC2007 and COCO2017. The experimental results demonstrate the effectiveness of RCGrid R-CNN in producing a high-quality anchor box.
Data Mining Techniques to Categorize Single Paragraph-Formed Self-narrated Stories

In this age of natural language processing, most of the sentiment analysis tasks are done by polarization, for example, 0 for negative or 1 for positive of the given context/text. In some work, the tasks are done using fine-grained polarization, such as very negative or very positive. The proposed system of this paper includes the categorization of the paragraphs using its nature. All the paragraphs are self-narrated, and the number of words in those self-narrated paragraphs contains 50–4200 words. The paragraphs are categorized using three categorizations: “work stress,” “bullying,” and “sexual harassment” in both real and cyber worlds. Artificial neural network paragraph vectors, a distributed bag-of-words and distributed memory, are used to get the embedding of each paragraph and later for classification by data mining techniques. The accuracy of each algorithm lies between 70 and 94%. The best model gives a 77.46% F1 score in the test set.
Large-scale clustering using decomposition-based evolutionary algorithms

Large-scale clustering is a challenging problem for many modern clustering algorithms. The majority of approaches reduce the clustering problem to the optimization problem, which is non-linear, multi-dimensional, and multi-extreme. Evolutionary algorithm based clustering is known as one of the most efficient approaches. Nevertheless, many modern evolutionary algorithms demonstrate low performance when solving large-scale global optimization problems. In this paper, we propose an approach for solving large-scale clustering problems using decomposition-based evolutionary algorithms. We have compared the performance of the standard k-means, k-means++, and the proposed CC-SHADE and CC-SHADE-RAG2 algorithms using benchmark problems with 16 clusters and 512 and 1024 attributes (the corresponding optimization problems use 8192 and 16384 objective variables). The experimental results show the decomposition-based approaches essentially outperform the standard evolutionary and k-means clustering algorithms.
A Self-supervised Framework for Human Instance Segmentation

Existing approaches for human-centered tasks such as human instance segmentation are focused on improving the architectures of models, leveraging weak supervision or transforming supervision among related tasks. Nonetheless, the structures are highly specific and the weak supervision is limited by available priors or number of related tasks. In this paper, we present a novel self-supervised framework for human instance segmentation. The framework includes one module which iteratively conducts mutual refinement between segmentation and optical flow estimation, and the other module which iteratively refines pose estimations by exploring the prior knowledge about the consistency in human graph structures from consecutive frames. The results of the proposed framework are employed for fine-tuning segmentation networks in a feedback fashion. Experimental results on the OCHuman and COCOPersons datasets demonstrate that the self-supervised framework achieves current state-of-the-art performance against existing models on the challenging datasets without requiring additional labels. Unlabeled video data is utilized together with prior knowledge to significantly improve performance and reduce the reliance on annotations. Code released at: https://github.com/AllenYLJiang/SSINS.
Internet search and lexical-semantic processing of analogs when making decisions in various subject areas

A mathematical model of Internet search and lexical-semantic processing of analogs of the decisions made in various subject areas is proposed. The search is carried out on queries synthesized from descriptions of problem situations. The synthesis process is carried out in two stages. At the first stage, sentences from the text containing a description of the problem situation are classified taking into account the informativeness of the verbal association between them. At the second stage, the information content of each class is calculated. The most informative of the generated classes, after their indexing, are used as queries. In lexical-semantic processing, the found analogs of solutions are examined for sentiment. When assessing sentiment, linguistic dictionaries of tone-colored vocabulary are used, which are formed on the basis of special tone-colored thematic corpora of texts. In the extreme case, two types of dictionaries are created. The first type is intended for the analysis of the positive sentiment in the descriptions of the decisions made, and the second is intended for the analysis of the negative sentiment, that is, in the process of lexical-semantic analysis, mainly positive and negative aspects of the decisions made are considered. The results of the analysis are presented to the user (decision-maker).