Reviewerly/exHarmony huggingface dataset:
Activity Prediction for Elderly Using Radio-Frequency Identification Sensors

In hospitals and nursing homes, older people usually fall due to weakness and disease. Standing or walking for a long time can be two of the many reasons for falling. One of the better ways for fall prevention is to monitor patient movement. A new kind of batteryless light sensors is providing us with new opportunities for activity prediction, where the inconspicuous nature of such sensors makes them very suitable for monitoring the elderly. In our study, we analyze such sensors known as radio-frequency identification (RFID) tags to predict the movements. We try to study a dataset obtained from 14 healthy old people between 66 and 86 years of age who were asked to wear RFID sensors attached with accelerometers over their clothes and were asked to perform a set of pre-specified activities. This study illustrates that the RFID sensor platform can be successfully used in activity recognition of healthy older people.
Research on Intelligent Operation and Maintenance Management of Rail Telescopic Regulator

The regional track structure of the beam-end telescopic adjuster on the long-span bridges and the rail-lifting device at the beam joint is prone to disease. Through the in-depth discussion and research on the application of Building Information Model (BIM) technology in the high-speed railway rails telescopic regulator project, combining the regulator health monitoring with BIM technology, a BIM-based regulator monitoring method was proposed. It uses edge computing to preprocess the data, adopts a hierarchical design, combines each functional domain with each layer of BIM in parallel. It establishes a universal security chain association model, and it conducts chain domain and global domain analysis of the data. On this basis, according to the Construction Operations Building Information Exchange (COBie) standard, an as-built delivery information model is established to integrate and transfer data. It seamlessly transfers the construction and management process information about the rail telescopic regulator and BIM to the operation and maintenance stages. In this way, the real-time assessment and early warning of the rail telescopic regulator and the coordinated management of intelligent decision-making are realized, laying a foundation for future intelligent operation and maintenance.
Apprendimento dell’italiano in Camerun

Linguistic research has demonstrated the importance of theories of language interference and positive or negative transfers from one language to another. Our study is based on written texts produced by the Italian language students of the ENS of the University of Maroua. The observed data indicated a clear influence of French and especially of its local variants over the use of idiomatic expressions. It is therefore legitimate to ask whether the lexical and syntactic deviations of French-speaking learners of Italian as a foreign language in Cameroon are only the result of a closeness of the two linguistic systems, or of an inadequate competence in the French language. The purpose of our work is to analyze the causes of such interference and possibly to draw useful strategies for the teaching of Italian in Cameroon.
Twitter Bots and the Swedish Election

In this chapter, we present a study of how political Twitter bots were used before the Swedish general election in 2018. We have not restricted our study to bots that are a software program instead, we are interested in any type of bot-like automated behavior. This includes a human that manually copies or retweets content repeatedly in a robot-like way to influence the interaction between a user and content or with other users. Our results show that bots were more likely to express support towards the immigration-critical party the Sweden Democrats, compared to genuine accounts.
Methodology of Activity Recognition: Features and Learning Methods

Sensor-based Human Activity Recognition (HAR) has been explored by many research communities and industries for various applications. Conventional pattern recognition approaches based on handcrafted features contributed a lot in this research field by employing general classification approaches. This chapter represents those handcrafted features in time and frequency domain along with their importance and feature selection methods. Following these methods, this chapter provides explanations of several conventional machine learning techniques for classifying sensor data for activity recognition. The problems of overfitting and underfitting have been discussed with remedies. Previous research works using conventional pattern recognition (PR) approaches on some benchmark datasets have also been analyzed.
An Flexible Selection Framework for Constrained Differential Evolution

Constrained optimization problems(COPs) is an important type of optimization problems. The swarm intelligence optimization algorithms are used to solve the problem and have achieved good results. Differential evolution(DE) is one of the algorithms and many improved constrained differential evolution(CDE) algorithms are proposed. Most of these algorithms only consider the use of constraint technology in individual updating. But like unconstrained optimization, the algorithm needs to consider the superiority and inferiority of the individuals when selecting individuals to improve performance. In order to solve this problem, an flexible selection framework for CDE is proposed in this paper. The framework measures merits and demerits of the individual from two aspects:fitness and diversity. And a flexible control method is used to control tendency of these two aspects. Experimental results show that the method has achieved good results in both the original CDE and improved CDE.
The Establishment of Core Curriculum Group of Chemical Engineering and Technology and the Exploration and Practice on the Joint Teaching Model in the Group

The paper discusses the internal correlation among core curriculums in the chemical engineering and technology.By integrating courses, joint construction and teaching, a main line is found to enhance the engineering abilities of students.And the establishment of curriculum group has strengthened the correlation among courses, avoiding course overlapping as well as the waste of class hour.It adopts a teaching model characterized by joint teaching in the group, and analysis of typical cases, which integrates all knowledge, enabling students to realize the significance and practicality of expertise in the practical projects, arouse their enthusiasm, and raise their consciousness of engineering and innovation.
MeshLifter: Weakly Supervised Approach for 3D Human Mesh Reconstruction from a Single 2D Pose Based on Loop Structure

In this paper, we address the problem of 3D human mesh reconstruction from a single 2D human pose based on deep learning. We propose MeshLifter, a network that estimates a 3D human mesh from an input 2D human pose. Unlike most existing 3D human mesh reconstruction studies that train models using paired 2D and 3D data, we propose a weakly supervised learning method based on a loop structure to train the MeshLifter. The proposed method alleviates the difficulty of obtaining ground-truth 3D data to ensure that the MeshLifter can be trained successfully from a 2D human pose dataset and an unpaired 3D motion capture dataset. We compare the proposed method with recent state-of-the-art studies through various experiments and show that the proposed method achieves effective 3D human mesh reconstruction performance. Notably, our proposed method achieves a reconstruction error of 59.1 mm without using the 3D ground-truth data of Human3.6M, the standard dataset for 3D human mesh reconstruction.
On Expected Polynomial Runtime in Cryptography

A common definition of black-box zero-knowledge considers strict polynomial time (PPT) adversaries but expected polynomial time (EPT) simulation. This is necessary for constant round black-box zero-knowledge in the plain model, and the asymmetry between simulator and adversary an accepted consequence. Consideration of EPT adversaries naturally leads to designated adversaries, i.e. adversaries which are only required to be efficient in the protocol they are designed to attack. They were first examined in Feige's thesis [], where obstructions to proving security are shown. Prior work on (designated) EPT adversaries by Katz and Lindell (TCC'05) requires superpolynomial hardness assumptions, whereas the work of Goldreich (TCC'07) postulates "nice" behaviour under rewinding.
Comparative study between super-resolution based on polynomial interpolations and Whittaker filtering interpolations

Super-resolution techniques are an important class of image processing techniques with the aim to enhance the resolution of images and videos in computer vision. In this survey, we aim in the paper to perform implementation techniques based on the parameterization and a comparison between the important polynomial interpolation algorithms and by Whittaker filtering applied in the case of mono-frame and multi-frame super-resolution. The super-resolution methods studied in the present work have the advantage of being both simple and of good performance in terms of resources consumed, particularly when it comes to the size of the processor and the required memory which are limiting factors. Also, given the simplicity of the used algorithms, their integration into embedded systems is highly favored.
An effective variable transfer strategy in multitasking optimization

As an emerging paradigm in evolutionary computation, multitasking evolutionary algorithm can solve multiple self-contained tasks simultaneously. Its performance has largely relied on task similarity. In this paper, a novel variable transfer strategy is proposed to boost the task similarity and reduce negative transfer of useful knowledge between tasks. The experiment results on nine instances revealed that, the proposed strategy has great exploitation ability, enhancing the convergence speed.
Analyzing and Improving the Network Flow Model in Hex Game System

The key problem in determining Hex game ability is to design a satisfactory evaluation function. Since the network flow model can reflect the connectivity of the system, and Hex is a connectivity game, some previous researchers proposed the idea of applying the network flow model to Hex. However, in the actual game, it was found that the game ability was poor, and the satisfactory results were often not given. This paper, first of all, analyzes the shortcomings of the network flow model applied to Hex and summarizes three main reasons for its poor evaluation ability. In order to improve the game ability of the system, this paper proposes a multi-level priority queue evaluation model. The main idea is as follows: firstly, according to the status around the stones, the stones are divided into five kinds of priority and put into the corresponding priority queue. Then the stones of the same priority are sorted by the priority queue custom sorting function. Finally, the best decision is screened by the select function. The experimental and evaluation results show that the game ability of the multi-level priority queue model is significantly better than that of the network flow model. Besides, the new evaluation model provides a new idea for the design of the evaluation function of Hex.
Phenotypic and genotypic detection of malaria parasite among patients attending Aminu Kano Teaching Hospital, Kano, Northwest Nigeria

Introduction: Malaria parasite (Plasmodium species) is one of the most important parasitic infections affecting people of all age groups throughout tropical countries wherever suitable hosts are found. The parasites are transmitted to man by the malaria vector, the female Anopheles mosquito during blood meals. Among all the members of the genus Plasmodium, Plasmodium falciparum is the most prevalent and common malaria species worldwide, especially in Africa and other developing countries. Aims and Objective: The present study aimed at the phenotypic and genotypic detection of malaria parasite among patients attending the General Outpatient Department of Aminu Kano Teaching Hospital, Kano, Northwest Nigeria. Material and Method: A total of 456 samples were collected from patients that voluntarily consent to participate in the study. Blood samples were randomly collected by trained personnel by vein puncture; and thick and thin smear was made on a clean glass slide and left to air dry. The smear was stained with Giemsa stain and observed under × 100 microscopic objective lens. A total of 41 (9.0%) positive malaria parasite smears were obtained with 0–9 years. Result: From 41 positive malaria parasite smears obtained by microscopic examination, 15 were randomly selected and confirmed by polymerase chain reaction (PCR) and ten were selected from negative microscopic smears. Eight (53.3%) out of the 15 positive smears and 3 (30.0%) from the negative smears were confirmed to be PCR positive. Conclusion: The finding of the study confirmed the presence of malaria parasite among the patient group in the study area using both phenotypic and genotypic detection techniques.
Convolutional Neural Network based Matchmaking for Service Oriented System Construction

In recent years, service-oriented computing, as a new computing paradigm, has been developed rapidly. Following this trend, more and more Web services and cloud services have been developed and made publicly available on the Web. These publicly available services will be important components for service-oriented system construction. However, these large numbers of services increase the burden of service selection in service-oriented system construction. This paper proposes a convolutional neural network-based service matchmaking approach to match services according to developer requests in service-oriented system construction. In the model, the convolutional neural network aims to learn the semantic feature representation for matchmaking. We experimented on a real-world dataset, ProgammableWeb.com, and experiment results show that the proposed approach can help find relevant services according to developer requests.
An Efficient Convolution Engine based on the À-trous Spatial Pyramid Pooling

This paper presents an efficient hardware architecture able to perform 2D dilated convolutions and suitable for the integration within modern heterogeneous embedded systems targeting semantic image segmentation. The proposed design supports multiple dilation rates. Moreover, it uses limited amounts of resources even when large convolution windows are processed. As a case study, the novel circuit has been integrated within a Xilinx Zynq-7000 FPSoC device to accelerate a state-of-the-art CNN model for medical images segmentation. Obtained results demonstrate that higher computational capabilities, reduced resources utilization and lower power consumption are achieved with respect to the competitors existing in literature.
A Simple Domain Shifting Network for Generating Low Quality Images

Deep Learning systems have proven to be extremely successful for image recognition tasks for which significant amounts of training data is available, e.g., on the famous ImageNet dataset. We demonstrate that for robotics applications with cheap camera equipment, the low image quality, however, influences the classification accuracy, and freely available data bases cannot be exploited in a straight forward way to train classifiers to be used on a robot. As a solution we propose to train a network on degrading the quality images in order to mimic specific low quality imaging systems. Numerical experiments demonstrate that classification networks trained by using images produced by our quality degrading network along with the high quality images outperform classification networks trained only on high quality data when used on a real robot system, while being significantly easier to use than competing zero-shot domain adaptation techniques.
Automatic Selection of Tuning Plugins in PTF Using Machine Learning

Performance tuning of scientific codes often requires tuning many different aspects like vectorization, OpenMP synchronization, MPI communication, and load balancing. The Periscope Tuning Framework (PTF), an online automatic tuning framework, relies on a flexible plugin mechanism providing tuning plugins for different tuning aspects. Individual plugins can be combined for convenience into meta-plugins. Since each plugin can take considerable execution time for testing various combination of the tuning parameters, it is desirable to automatically predict the tuning potential of plugins for programs before their application. We developed a generic automatic prediction mechanism based on machine learning techniques for this purpose. This paper demonstrates this technique in the context of the Compiler Flags Selection plugin, that tunes the parameters of a user specified compiler for a given application.
Vapnik–Chervonenkis dimension and density on Johnson and Hamming graphs

VC-dimension and VC-density are measures of combinatorial complexity of set systems. VC-dimension was first introduced in the context of statistical learning theory, and is tightly related to the sample complexity in PAC learning. VC-density is a refinement of VC-dimension. Both notions are also studied in model theory, in the context of dependent theories. A set system that is definable by a formula of first-order logic with parameters has finite VC-dimension if and only if the formula is a dependent formula. In this paper we study the VC-dimension and the VC-density of the edge relation Exy on Johnson graphs and on Hamming graphs. On a graph G, the set system defined by the formula Exy is the vertex set of G along with the collection of all open neighborhoods of G. We show that the edge relation has VC-dimension at most 4 on Johnson graphs and at most 3 on Hamming graphs and these bounds are optimal. We furthermore show that the VC-density of the edge relation on the class of all Johnson graphs is 2, and on the class of all Hamming graphs the VC-density is 2 as well. Moreover, we show that our bounds on the VC-dimension carry over to the class of all induced subgraphs of Johnson graphs, and to the class of all induced subgraphs of Hamming graphs, respectively. It also follows that the VC-dimension of the set systems of closed neighborhoods in Johnson graphs and Hamming graphs is bounded. Johnson graphs and Hamming graphs are well known examples of distance transitive graphs. Neither of these graph classes is nowhere dense nor is there a bound on their (local) clique-width. Our results contrast this by giving evidence of structural tameness of the graph classes.
Course Selection System Based on Memory Data Access Technology

Taking the course selection system of South china Institute of Software Engineering of Guangzhou University as an example, the underlying data interaction technology of the system and its advantages and disadvantages are analyzed, and an optimization scheme is proposed based on this. The optimization solution uses the fast access feature of memory data to pre-load necessary data into memory, uses ConcurrentHashMap to improve the system's concurrency and processing efficiency, uses locks to ensure that lock acquisition is performed in the order of requested locks, and then uses the message queue component Rabbitmq to asynchronously memory data Update to the background database. Experimental results verify the feasibility and effectiveness of the scheme.
Towards Computing Inferences from English News Headlines

Newspapers are a popular form of written discourse, read by many people, thanks to the novelty of the information provided by the news content in it. A headline is the most widely read part of any newspaper due to its appearance in a bigger font and sometimes in colour print. In this paper, we suggest and implement a method for computing inferences from English news headlines, excluding the information from the context in which the headlines appear. This method attempts to generate the possible assumptions a reader formulates in mind upon reading a recent headline. The generated inferences could be useful for assessing the impact of the news headline on readers, including children. The understandability of the current state of social affairs depends significantly on the assimilation of the headlines. As the inferences that are independent of the context depend mainly on the syntax of the headline, dependency trees of headlines are used in this approach, to find the syntactic structure of the headlines and to compute inferences out of them. Considering the headline as the entry point to a piece of news and a source rich information about the news, we explored a headline’s potential of giving an idea about the current state of affairs, leveraging the syntax structure.
Mess Mass Nouns

Mess mass nouns are interpreted as mess massMess mass i-sets. A mess massMess mass i-set is an i-set with a base that is either not atomisticAtomistic (or even not atomicAtomic) or is atomisticAtomistic with a set of base atoms that is not disjoint. The class of mess massMess mass i-sets is an eclectic category which allows a spectrum of techniques for modeling the semantics of mess mass nounsMess mass nouns. Section 8.1 gives examples of mess massMess mass analyses across this spectrum. In many of these cases the base of the i-set is an overlapping set of variantsVariants for the i-set supremum, alternative ways of partitioning the i-set supremum into disjoint sets of parts. In Sect. 8.1.5 we apply the theory to the Supremum argumentSupremum argument that was discussed in Chap. 4 , and show how the Iceberg semanticsIceberg semantics given here allows both an analysis where the supremums of the interpretations of corresponding mass and count DPs are identified, and an analysis where they are not. Section 8.2 deals with grindingGrinding or downshiftingDownshifting interpretations. DownshiftingDownshifting is an operation that extends body and base of an i-set downwards. It is shown that we find downshiftingDownshifting both for count nouns and for neat mass nounsNeat mass nouns and it is argued that downshiftingDownshifting is always to mess massi-setmess mass. Section 8.3 argues that in Dutch under certain circumstances count comparisonCount comparison and modification with distributive adjectivesDistributive adjectives is possible even for mess mass nounsMess mass nouns: a highly salient contextual disjoint portioningPortioning of the body of the mess mass nounMess mass nouns denotation can access a disjoint distribution setDistribution set of portionsPortions, and hence allow what is normally only allowed for neat mass nounsNeat mass nouns.
Efficient Deployment of a Web Application in Serverless Environment

In the recent era to gain speed up, scale-up/down, and for cost-effective features, people are moving toward services provided by AWS or other computing paradigms, (salesforce, etc.) i.e., AWS Lambda which is applicable to build a serverless environment. Serverless environment doesn’t mean there is no server but tells AWS to not only give all services which are provided by the server but also give metering on a pay per second basis. This project will entertain users with a full stack-based interface where they can request a unicorn ride from any place they want.