Reviewerly/exHarmony huggingface dataset:
Research on the method of detecting and eliminating gross errors in Beidou monitoring time series

In deformation monitoring work, there are often gross errors in the monitoring data sequence. Due to the complex distribution of the monitoring data sequence, the conventional gross error detection method is not ideal in the processing of the monitoring data sequence. Based on this, the improved 3σ method based on wavelet analysis is proposed to achieve the purpose of identifying and eliminating gross errors. The experimental results show that the method can identify and eliminate the gross error signals that are randomly added, and it can fit the overall trend of the detail signal and the data signal well. The simulation example of the algorithm illustrates its trend extraction. The accuracy fully meets the requirements of gross error identification and elimination.
More About Asymptotic Properties of Some Binary Classification Methods for High Dimensional Data

In this manuscript we study the asymptotic behavior of the following binary classification methods: Support Vector Machine, Mean Difference, Distance Weighted Discrimination and Maximal Data Piling, when the dimension of the data increases and the sample sizes of the classes are fixed. We consider multivariate data with the asymptotic geometric structure of n-simplex, such that the multivariate standard Gaussian distribution, as the dimension increases and the sample size n is fixed. We provide the asymptotic behavior of the four methods in terms of the angle between the normal vector of the separating hyperplane of the method and the optimal direction for classification, under more general conditions than those of Bolivar-Cime and Cordova-Rodriguez (Commun Stat Theory Methods 47(11):2720–2740, 2018). We also analyze the asymptotic behavior of the probabilities of misclassification of the methods. A simulation study is performed to illustrate the theoretical results.
Prediction and Analysis in High Utility Pattern Mining Using List Based HUPM Algorithm

HUPM has been researched successfully in the field of processing data. Traditional Pattern Mining cannot think fully of the features of the databases that have been used in the real world. In contrast, in many applications industries and interaction with details on internet services, the database sizes are increasingly closer to retail sales data by a little bit. This is a general approach that is not adequate for static databases to prepare complex datasets and retrieve useful data. Most incremental utility pattern mining strategies have been developed. It has been proposed that previous methodologies, independent of the use of any framework, require more scanning for incremental utility sequence mining. Guy. In either case, there are proposed for different scans. It's just not enough for stream conditions. We have suggested and Effective algorithm that uses mine High utility trends based list data structure to involving just one search of the database and interested user restrictions limit the space search. This is the Transactional Retail Algorithm uses the database. We add conditions to our method's efficiency, such as duration, object date that helps make forecasts more precise.
Finding the Path Toward Design of Synergistic Human-Centric Complex Systems

Modern decision support systems are becoming increasingly sophisticated due to the unprecedented volume of data that must be processed through their underlying information architectures. As advances are made in artificial intelligence and machine learning (AI/ML), a natural expectation would be to assume that the complexity and sophistication of these systems will become daunting in terms of comprehending their design complexity, effective operations, and managing total lifecycle costs. Considering the fact that such systems operate holistically with humans, the interdependencies created between the information architectures, AI/ML processes and humans begs that a fundamental question be asked –“how do we design complex systems such as to yield and exploit effective and efficient human-machine interdependencies and synergies?” A simple example of these interdependencies may include the effects of human actions changing the behavior of algorithms and vice-versa. The algorithms may serve in the extraction and fusion of heterogeneous data, employ a variety of AI/ML algorithms that range from hand crafted, supervised and unsupervised approaches coupled with federated models and simulations to reason and infer about future outcomes.The purpose of this chapter is to gain a high-level insight into such interdependencies by examining three interrelated topics that can be viewed as working in synergy towards the development of human-centric complex systems: Artificial Intelligence for Systems Engineering (AI4SE), Systems Engineering for Artificial Intelligence (SE4AI), and Human Centered Design (HCD) and Human Factors (HF). From the viewpoint of AI4SE, topics for consideration may include approaches for identifying the design parameters associated with a complex system to ensure code maintainability, to minimize unexpected system failures, and to ensure that the assumptions associated with the algorithms are consistent with the required input data while optimizing the appropriate level of interaction and feedback from the human. Considering SE4AI, how can the synergies between different AI/ML approaches from handcrafted rules to strictly data-driven learning within the data-to-decisions information pipeline be realized, again while maximally leveraging human inputs? From the lens of HCD and HF, a system is likely to be necessarily complex, and a key aspect of the designer may be to ensure an optimal balance between the human systems or software developer and the end-user. For instance, can principles from HCD/HF engineering permit us to design better systems that enhance end-users’ strengths (e.g., intuition, novel thinking) while helping to overcome their limitations (e.g., helping a user maintain focus and attention during tasks that require significant multi-tasking)?
Investigating lay evaluations of models

Many important decisions depend on unknown states of the world. Society is increasingly relying on statistical predictive models to make decisions in these cases. While predictive models are useful, previous research has documented that (a) individual decision makers distrust models and (b) people’s predictions are often worse than those of models. These findings indicate a lack of awareness of how to evaluate predictions generally. This includes concepts like the loss function used to aggregate errors or whether error is training error or generalisation error. To address this gap, we present three studies testing how lay people visually evaluate the predictive accuracy of models. We found that (a) participant judgements of prediction errors were more similar to absolute error than squared error (Study 1), (b) we did not detect a difference in participant reactions to training error versus generalisation error (Study 2), and (c) participants rated complex models as more accurate when comparing two models, but rated simple models as more accurate when shown single models in isolation (Study 3). When communicating about models, researchers should be aware that the public’s visual evaluation of models may disagree with their method of measuring errors and that many may fail to recognise overfitting.
SRDD-Net: A complete pipeline for depth-maps based dynamic hand gesture recognition

The widespread use of technology in hospitals and the difficulty of sterilizing computer controls has increased opportunities for the spread of pathogens. This leads to an interest in touchless user interfaces for computer systems, especially the application of gesture recognition to human-machine interfaces. Human gestures are a universal language that requires minimal time to learn. This research work proposes an end-to-end data-driven deep-learning model called SRDD-Net to address the problem of gesture recognition, composed of an encoder network to encode germane hand features, and a classification model to decode the dynamic hand gestures from a string of encodings. Since classification of images is more computationally expensive than encoding the images, our approach implements a hand pose estimation model to estimate 21 key hand-point coordinates in the image that are later fed into a spatio-temporal classification model to provide a more economical and an accurate method for gesture classification.
Pervasive Healthcare Computing and its Contribution to Hospitals, Chronic and Preventive Care

Pervasive computing denotes to the group of technologies intended to soundly incorporate health training, involvements, as well as observing technology into our monotonous lives, irrespective of locality and time. The objective can solitarily be attained once the patient-based data is made accessible at the fingertips of the physicians, healthcare specialists, and others contributing to increase the health of the people. This methodology can grow mutually the handling area and overall superiority of care. Pervasive healthcare computing delivers the canopy structure to define the complete supervision of patient health information as well as its protected exchange among doctors, healthcare professionals, nurses, and hospital managements. In excess of the previous decade, pervasive computing outcomes intended for healthcare have become progressively dominant in both investigation and profitable efforts. Taking into consideration the working circumstances of general practitioner and the requirements of related patients, pervasive computing provides a number of eye-catching solutions for a number of challenges to care supply in these areas. This chapter analyzes, assesses, and discusses a multiplicity of investigation works and profitable results dedicated to sympathetic, scheming, as well as applying pervasive healthcare applications in support of hospital care, chronic care, and preventive care.
Two-way Augmented Reality Co-location Under Telemedicine Context

The medical care responsibilities are often on the shoulders of nonprofessionals such as captains who are equipped with forty hours of designated training every five years. However, this training is neither enough for the captains to handle medical incidents nor releases their stress during the treatment. Currently, captains have very limited support from a medical expert, only via phone call or email from the Radio Medical Services. Thus, the authors explored that how the two-way augmented reality (AR) can support the collaboration between captains and doctors for a better quality of care. A Human-Centred Design approach is applied in this study, including field study and user testing. The lean user experience method was applied with fast prototyping-testing loops. The main findings are AR played an essential role to boost confidence on the captain's side, and the real value of AR is in supporting medical skills like suturing and abdominal searching. This study serves as a pilot research, thus it was limited by small sample size and qualitative method. Improving the communication between the captains and doctors is key for future studies.
Sustainable Platform for Environmental Changes in Smart City

In the world of human evolution, species with excellent physical and brain abilities do not survive, but only species with attributes that can adapt to the environment at each specific trend can survive. Therefore, in this paper, we propose the future smart city concept and structure, based on mechanism in human evolution. Our proposed platform consists of, 1) “the large archive which consists of many different AI models”, and 2) “various trials and update of AI models”. Then, we deploy these two functions into three hierarchy, i.e. cloud stratum, MEC (Multi-access Edge Computing) stratum and Fog stratum. And these stratums should be nationwide common open platform. Consensus buildings are performed among the tracking and learning of Cloud stratum (wider & long-term in space-time zone), MEC (medium-term in space-time zone) and Fog (narrow & short-term in space-time zone) stratums. At the same time, according to the update of the currently used model, in cloud stratum, various trials are continuously operated in parallel, as background process. New AI models and modules are created, using the gathered historical data, the existing AI models and just finished AI modules. In addition, for this creation of new models, federated learning is carried out through AI, human control/knowledge and the social world. The federation/ensemble/transfer among the followings, bring about another mechanism of Human evolution: a) The large archive which consists of many different models, b) Various trials and update of models, c) Human knowledge/Intervention, and d) Social world (trend, opinion). If the existing models and modules can't cope with the big change, then the new model, which is the federation/ensemble/transfer of AI, human professional knowledge and social under the observed ongoing trend, appear. These federation/ensemble/transfer are effective for not only the adaptation to the new trend, but also the design of future smart city.
Algorithm for Group Planning of Movement on Uneven Surface for Ground Robots

This paper considers the problem of energy-efficient path planning for a group of ground robots in a three-dimensional environment. To solve it, an algorithm for planning the group movement of ground robots on a relief surface GPMUS (Group Planning of Movement on Uneven Surface) is proposed. This algorithm is based on the LRLHD-A* and ORCA-3D algorithms developed by the authors. The primary paths of the robots are built using the LRLHD-A* (Local Roughness Local Height Difference A*) algorithm, designed to plan an energy efficient path on uneven surfaces. To construct collision-free trajectories for a group of robots, which are based on the paths calculated by LRLHD-A*, it was proposed to use a modification of the ORCA (Optimal Reciprocal Collision Avoidance) algorithm - ORCA-3D. This algorithm uses the difference in height between adjacent vertices of the path to calculate the correct length of the 2D velocity vector for each robot, which provides more accurate positioning of the robots at each time step. When GPMUS was tested in the Gazebo simulator, it was found that the energy efficiency of the trajectory of the robots increased in the range from 1.3 to 6.3%, and the average deviation of the positions of the robots at each time step from the tops of the paths decreased in the range from 10 to 57%.
ALL Dolphins Are Intelligent and SOME Are Friendly: Probing BERT for Nouns’ Semantic Properties and their Prototypicality

Large scale language models encode rich commonsense knowledge acquired through exposure to massive data during pre-training, but their understanding of entities and their semantic properties is unclear. We probe BERT (Devlin et al., 2019) for the properties of English nouns as expressed by adjectives that do not restrict the reference scope of the noun they modify (as in “red car”), but instead emphasise some inherent aspect (“red strawberry”). We base our study on psycholinguistics datasets that capture the association strength between nouns and their semantic features. We probe BERT using cloze tasks and in a classification setting, and show that the model has marginal knowledge of these features and their prevalence as expressed in these datasets. We discuss factors that make evaluation challenging and impede drawing general conclusions about the models’ knowledge of noun properties. Finally, we show that when tested in a fine-tuning setting addressing entailment, BERT successfully leverages the information needed for reasoning about the meaning of adjective-noun constructions outperforming previous methods.
Challenges and Opportunities for Unikernels in Machine Learning Inference

Machine Learning has become a value creator for many new and old businesses. However, efficient realworld machine learning deployments are still a challenge. Traditional Machine Learning deployments suffer from efficient resource utilization and achieving predictable latency. They cannot be treated in the same manner as other application server deployments. Unikernels are a method to specialize application deployment and performance to suit the needs of the application. Traditionally, building or porting applications to unikernels have been challenging. However, recent work has been into simplifying the development of unikernels. Real-world Unikernels as of now are only for specializing applications that run on the CPU. We survey machine learning practitioners and find out that the majority of machine learning practitioners are using the CPU for machine learning deployments, thus, creating an opportunity for unikernels to optimize the performance of these applications. We compare the architecture of two unikernels: nanos and Unikraft. We benchmarked scikit-learn, a popular machine library, inside a unikernel and found that it only offered a 1% advantage over a traditional deployment. However, our testing could not include more innovative systems like Unikraft due to their immaturity and inability to run machine learning libraries. We include a dependency analysis of three popular machine learning libraries Tensorflow Lite, PyTorch and ONNX, to help pave the way for building machine learning applications as Unikraft unikernels.
A Modern Approach to Application Specific Processors for Improving the Security of Embedded Devices

The security of embedded devices is very important today because critical infrastructures like medical devices, transportation, communication, and smart grids deploy them in large numbers. In all of these areas cybersecurity is the enabler for safety as failing or compromised devices threaten the life of people. Often, it is not feasible securing already deployed devices because of high costs, missing physical access, or defective hardware. Therefore, the next generation devices deployed in the future have to be more secure and more resilient to cyber attacks.This paper proposes a new approach to secure these next generation devices by using program optimized Application Specific Processors (ASPs) and employing an easy to integrate development flow for embedded developers. It evaluates the security improvement by analyzing the vulnerability of the approach against typical attack patterns.
BoolHash: A New Convolutional Algorithm for Boolean Activations

Integer algorithms, where applicable, can both decrease the memory requirements and improve the speed of the convolutional neural networks (CNN). Boolean activations can further increase the speed gain. Here, we propose a convolutional algorithm called BoolHash. It is based on precalculated inference lookup tables (PCILTs). In addition, it uses activation merging to additionally increase the inference speed. We used a CNN with INT16 input weights, INT8 filter weights and boolean activations to compare the speed of BoolHash to that of a classic weight-adder (WA) convolutional algorithm.
Self-Attention based Multitasking Sequential Recom Mendation

An increasing number of recommender systems use the order of user-item interactions as a key feature to obtain the user’s next behavior. Markov chains generate a state transition matrix by viewing user interaction records, which predicts the next behavior. The recurrent neural network predicts the items that the user is most likely to be interested in next by revealing the semantics of the longer term in the user’s interaction record. The MC-based approach performs well in very sparse datasets,while RNN perform better in more dense datasets with higher model complexity. On the other hand, in real life, there are various relationships between items, these relationships can reveal the detailed knowledge of the item in a closer way. The work in this paper starts from these two aspects and proposes a sequence model based on self-attention that combines the relationships between items to capture both long-term semantics and predictions based on a few interaction records.
A novel weighted fuzzy c-means based on feature weight learning

In feature weighted fuzzy c-means algorithms, there exist two challenges when the feature weighting techniques are used to improve their performances. On one hand, if the values of feature weights are learnt in advance, and then fixed in the process of clustering, the learnt weights might be lack of flexibility and might not fully reflect their relevance. On the other hand, if the feature weights are adaptively adjusted during the clustering process, the algorithms maybe suffer from bad initialization and lead to incorrect feature weight assignment, thus the performance of the algorithms may degrade the in some conditions. In order to ease these problems, a novel weighted fuzzy c-means based on feature weight learning (FWL-FWCM) is proposed. It is a hybrid of fuzzy weighted c-means (FWCM) algorithm with Improved FWCM (IFWCM) algorithm. FWL-FWCM algorithm first learns feature weights as priori knowledge from the data in advance by minimizing the feature evaluation function using the gradient descent technique, then iteratively optimizes the clustering objective function which integrates the within weighted cluster dispersion with a term of the discrepancy between the weights and the priori knowledge. Experiments conducted on an artificial dataset and real datasets demonstrate the proposed approach outperforms the-state-of-the-art feature weight clustering methods. The convergence property of FWL-FWCM is also presented.
FlyTrap: A Blockchain-based Proxy for Authorisation and Audit of MQTT Connections

MQTT brokers have become integral components of many IoT systems. However, the authorisation mechanisms defined by the MQTT protocol remain optional which may result in weak security configurations of MQTT brokers. In addition, with increasing prevalence of IoT solutions across many domains, MQTT brokers are used to forward various types of data including material that may be personal or commercially sensitive. Such data may be subject to a number of regulations (e.g. GDPR) which require trusted and auditable reporting that goes beyond local log files maintained by brokers. In this paper, we explore the potential of blockchains to facilitate a trusted and secure support infrastructure for authorisation and audit of MQTT connections.
Chapter 9. Multiword expressions – a tough typological nut for Swedish FrameNet++

Multiword expressions have attracted much attention in language technology over the last two decades or so, and in general linguistics, the interest in phraseology – which includes the linguistic study of multiword expressions – goes back much further. In our work on the multilingual components of Swedish FrameNet++, we have strived to adopt a typologically informed view on multiword expressions. This raises a number of theoretical and methodological questions, some of which are discussed in this chapter.
Photodetector based Auto Trigger System using Kalman Filter

Synchronous triggering of multiple tracking systems is an important task that allows simultaneous data capture in a test range. In addition, it is very much necessary for initiation of tracking systems, which require synchronization to target movement. The aim of this paper is to elucidate about the novel Auto Trigger System designed to generate trigger pulse automatically with consideration of making it cost effective, by using raspberry Pi, i.e. a single board computer. In this auto trigger system, the authors utilize two Raspberry Pi's; one as processor as well as transmitter and another as receiver. Optical detector of Photodiode type is used for detection of event initiation. Real time noise suppression and threshold estimation is done by using Kalman filter with outlier detection technique. This novel triggering system is feasible for deployment where synchronisation plays an important role for evaluation purpose.