Reviewerly/exHarmony huggingface dataset:
ANALYZING THE PREVALENCE AND INCIDENCE OF TYPHOID FEVER, MALARIA AND THEIR COINFECTIONS IN FEBRILE SUBJECTS OF URBAN INDIA: A CROSS-SECTIONAL STUDY

Background: Typhoid fever and malaria remain major public health threats in developing countries and tropical regions. In endemic regions, people are at a very high risk of developing both the disease at the same time with significant overlapping of associated symptoms and signs&#x0D; Aims: The present cross-sectional clinical trial was undertaken to assess the prevalence, typhoid fever, malaria, and their coinfections in urban Indian subjects with fever. &#x0D; Materials and Methods: In 180 subjects, demographic data and clinical features of all the study subjects were recorded. For assessing typhoid fever, a slide agglutination test was done using H (flagellar) and O (somatic) antigen kits for S.typhi. The collected data were subjected to statistical evaluation for results formulation.&#x0D; Results: Total positive malaria cases were 37.22% (n=67). Among 67 positive cases, 43.28% (n=29) were seen to have P.vivax, 40.29% (n=27) had P. Falciparum and mixed infection were seen in 16.41% (n=11) subjects. Typhoid and malaria coinfection was seen in 6.66% (n=12) of study subjects. On correlating typhoid fever, malaria, and coinfections with demographic characteristics and clinical characteristics in the study subjects, it was seen that the age groups of 2 years to 10 years were associated with malaria, and the association was statistically significant with p=0.03.&#x0D; Conclusion: Within its limitations, the present study concludes that the coinfections are common owing to the high prevalence and overlapping symptoms of malaria and typhoid fever. Poor hand washing habits are associated with typhoid fever significantly. &#x0D; Keywords: Coinfections, Malaria, P. Falciparum, S. typhi, Typhoid fever
Statistical Bias in D-Wave Qubits

Abstract Recent access to commercially available quantum computing capabilities offers the potential for testing a host of new algorithms that take advantage of fundamentally different processing approaches. One such computing resource is D-Wave’s quantum annealing computer, which employs an energy minimization-based computing mechanism to find solutions to problems stated in an Ising formulation. These solutions, however, must be interpreted in terms of Monte Carlo-style probabilistic occurrences and histograms of candidate solutions that presume the underlying statistics of the qubits are “fair.” This paper quantifies the underlying fairness of individual qubits on a single D-Wave machine, showing that there is a measurable bias in the resulting solution as a function of which qubits are used in the calculation. Further, an example problem is demonstrated, whereby the same problem formulation yields distinctly different solution probabilities by changing which physical qubits are employed.
Increasing International Networks of Universities: Emerging New Forms of University Cooperation

Growing global trends induced by globalisation and internationalisation have pushed universities to collaborate with others to ascertain their existence and competitiveness. Higher education institutions are increasingly expected to meet such global development as well as the local goals, depending on the needs. In this regard, new forms of interaction on national, regional, and international levels between higher education institutions are emerging. Such cooperation helps universities to share their resources for mutual benefits that would increase their efficiency. While such cooperation is not always easy and is posed with expected and unexpected challenges, which hinders such optimism. This study, therefore, aims to explore and account the nature, opportunities, and challenges of such new forms of university cooperation. The research relied on secondary data, mainly from the websites of the university networks, by exploring 33 international networks of universities, which includes 13 associations, 12 networks, and eight consortiums. The following criteria were used for analysis: the geographical and territorial distribution of network universities and types of interaction between partner universities and implemented projects. The findings indicate the existing problems in university cooperation, offer scope for the prospects of network universities, and define the measures for their future development. The study concludes that the availability of technological information, the communication platforms, and the adherence to the developed standards for the implementation of international projects are important for the effectiveness of these networks and their cooperation.
Enhancing Search Results with Knowledge Graphs

Knowledge graph usage in search engines is one of the widely adopted approaches for enhancing search results: the graph is populated and structured based on a domain, and the search can directly query this for finding relevant entities. Working with scientific publications data, where main entities such as authors, documents, organizations and topics, are interlinked and provide more context when treated together, we started to leverage this natural data structure by building a citation graph. In this presentation we are going to show how we are already using a citation graph in our data ingestion pipeline to enrich data stored in search engine index and how we plan to use the graph to improve overall search experience (i.e., by improving ranking, understanding context etc.).
Filling the Gap Between Business and Application Development Using Agile BORM

Agile software development is the most popular methodology used in today’s software engineering. Agility in software development does not mean the resignation to the accuracy and quality of meeting user requirements. However, it is apparent that the tasks of requirements engineering are taken lightly or often overlooked in agile projects due to project cost. One of the main reasons for this is the lack of efficient and easy to use modelling tools which can be used for the purposes of business modelling and initial software design. Therefore, there is a need for good modelling tools which can be easily used by both non-technical users and software developers which can fill the gap between business needs and identified software requirements. This paper presents BORM as a feasible solution for this in an agile software development environment.
The Analysis of the Online Platforms for Evaluating the Students’ Supra-Professional Competencies

Due to the active development of the digital technologies and automation, the changes are taking place in many areas of the human resources management, including the partial transfer of the personnel training and evaluation. Business technologies of the human resources management are beginning to be used by the educational organizations. It is getting obviously that it is important for the students to develop their supra-professional competencies at the very first stages, embedding them in each discipline. In this paper, the authors analyze the Russian language online platforms for assessing competencies, which are designed for business, but could be used by the universities to assess the level of the students’ supra-professional competencies. We are supposed that it is important to assess and develop them to active citizenship, personal self-realization, and employment opportunities in a knowledge-based society.
Effect on Solar PV Panel Performance Due to Varying Latitude in Northern Hemisphere

In the field of renewable energy, solar energy plays a major role in power generation. This study also focuses on the parameters of the PV panel which affect the efficiency of the PV panel. The optimum tilt angle and the factors like solar radiation and location’s latitude on which it depends are also considered in this study. The optimum tilt angle of 20 different sites in the northern hemisphere at different latitudes is found out through the software such as SolarGIS and PVsyst, and comparison is done between the power generation at optimum tilt angle and latitude’s location. The optimization of the tilt angle which is done by varying tilt angle to maximum solar irradiation. The optimum tilt angle of summer and winter season is also calculated and analyzed to maximize the incident solar radiation. The correlation coefficient between the location’s latitude and the optimum tilt angle is tilt through SolarGIS which is 0.953 and 0.972 in PVsyst.
Discussion on SOC system construction

By introducing the transformation of NOC to SOC, this paper analyzes the main contents of SOC system, and discusses the five general thoughts of changing operation thinking, determining construction objectives, setting up a team and formulating rules and procedures, and building a technology platform for the construction of SOC system.
Impact of multireflections on measurement accuracy in the endoscopic 3D reconstruction of gearing geometries

Optical triangulation systems based on fringe projection profilometry have emerged in recent years as a complement to traditional tactile devices. Due to the good scalability of the measurement approach, a highly compact novel sensor for maintenance and inspection in narrow spaces is realized by applying optical fiber bundles. Especially in the field of high-resolution and rapid maintenance in industrial environments, numerous applications arise. Endoscopic 3D measurements of gearing geometries are of particular technical relevance for detecting and quantifying damage. The measurement performance depends to a considerable extent on the technical surface to be inspected. Polished surfaces are particularly problematic due to specular reflections, but can still be partially reconstructed by using HDR imaging. However, if multiple reflections occur due to the specimen geometry and sensor arrangement in such a way that the optical path of each corresponding camera pixel can no longer be reconstructed unambiguously, a measurement is no longer feasible. In this study, the effects of surface roughness, sensor arrangement, and triangulation angle on measurement error are systematically investigated to describe possible application limits and provide guidance on sensor operation.
Research on Comprehensive Benefit Evaluation Model of Power Distribution Internet of Things Based on System Dynamics

Abstract Access of distributed energy to power distribution Internet of Things is of great significance to improving resource utilization and achieving emission reduction, and is an important means to improve the comprehensive benefits of distribution Internet of Things. Considering that the comprehensive evaluation of power distribution Internet of Things is a complex and dynamic process, this paper proposes a comprehensive evaluation model of power distribution Internet of Things based on system 1 dynamics. Firstly, according to the principle of system dynamics, the system dynamics index is established from the two parts of economic benefit improvement and social benefit drive, and the corresponding system causality diagram is drawn. Secondly, regression analysis is used to determine the system dynamics equation of the comprehensive evaluation index. Finally, the comprehensive evaluation method of analytic hierarchy process is used to analyze the comprehensive benefits of a provincial power distribution Internet of Things. The results show that the system dynamics model considering the correlation of each index makes the evaluation result more objective and more valuable for application.
Exploration about Whether the First Few Dimensions of Word Embedding Encode Frequency Information

On the Internet, there are massive amounts of text messages every day. NLP is the process of using computers to process natural language. Among them, word vectors are the core of many natural language processing tasks. How to effectively retain the abstract features of natural language is a research focus. In this paper, in order to further explore whether the first few dimensions of word embedding encode frequency information, we conduct a more detailed study to explore whether the first few dimensions of information are not semantically relevant. The experimental results show that there is no strong evidence that the first few dimensions encode frequency information; after a series of experiments, we think: the first few dimensions of the word vector may encode frequency information, and the frequency information about the word is probably encoded in the first three main dimensions of the word embeddings. But a more exact conclusion needs a deeper experiment to confirm.
An Algorithm Based on Monarch Butterfly Optimization with Learning Mechanism and Topological Structure

In the past decades, various attention has been paid to the global optimization problems. The Monarch Butterfly Optimization (MBO) algorithm is an effective meta-heuristic algorithm for the global optimization problems. However, in the MBO, the diversity of the population is lost in the late iteration. The MBO is easy to trap into the local optima. In this study, an algorithm based on MBO with learning mechanism and topological structure, named LTMBO, is proposed to enhance the ability of exploration and exploitation on the global optimization problems. The learning mechanism is present for the migration operator to increase the speed of the iteration. The topological structure is proposed for the butterfly adjusting operator to improve the diversity of the population. The experimental results demonstrated that the efficiency and significance of the proposed LTMBO algorithm.
Comparative Analysis of Lossless Image Compression Algorithms based on Different Types of Medical Images

In the medical field, there is a demand for highspeed transmission and efficient storage of medical images between healthcare organizations. Therefore, image compression techniques are essential in that field. In this study, we conducted an experimental comparison between two famous lossless algorithms: lossless Discrete Cosine Transform (DCT) and lossless Haar Wavelet Transform (HWT). Covering three different datasets that contain different types of medical images: MRI, CT, and gastrointestinal endoscopic images; with different image formats PNG, JPG and TIF. According to the conducted experiments, in terms of compressed image size and compression ratio, we found that DCT outperforms HWT regarding PNG and TIF format which represent CT-grey and MRI-color images. And regarding JPG format which represents the gastrointestinal endoscopic color images, DCT performs well when grey-scale images are used; where HWT outperforms DCT when color images are used. However, HWT outperforms DCT in compression time regarding all the image types and formats.
A Voice-Based Travel Recommendation System Using Linked Open Data

We introduce J.A.N.E. – a proof-of-concept voice-based travel assistant. It is an attempt to show how to handle increasingly complex user queries against the web while balancing between an intuitive user interface and a proper knowledge quality level. As the use case, the search for travel directions based on user preferences regarding cuisine, art and activities was chosen. The system integrates knowledge from several sources, including Wikidata, LinkedGeoData and OpenWeatherMap. The voice interaction with the user is built on the Amazon Alexa platform. A system architecture description is supplemented by the discussion about the motivation and requirements for such complex assistants.
Cost-Efficient Request Dispatching in Geo-distributed Cloud Gaming Infrastructure

Cloud gaming is a recent approach to gaming, where processing for the games occurs on well-equipped servers within the cloud and a video stream is returned to the user, meaning users can play high-end games on devices that lack computational power. Different games require different amounts of computational resources and computation times. It would be desirable to efficiently pack a number of servers with multiple games at once, however this is complicated within a geo-distributed cloud system as we must consider that not every data center can fulfil every game request due to latency requirements. Within this work, we present shadow routing algorithms to distribute game requests to cloud data centers and also to pack the servers within the data centers with these game requests. These algorithms are designed to operate in order to minimize total cost from server hire and bandwidth usage, and we prove their performance is asymptotically close to optimal. An experiment using realistic arrival rates is given, and the results verify our theory within a realistic context. Also shown using proof and experimentation is that the algorithms can adapt themselves to periodic changes as demand raises and falls while remaining close to the optimal, which is a particular weak point of other schemes.
Data Consultations, Racism, and Critiquing Colonialism in Demographic Datasheets

Objective: We consider how data librarians can take antiracist action in education and consultations. We attempt to apply QuantCrit thinking, particularly to demographic datasheets. Methods: We synthesize historical context with modern critical thinking about race and data to examine the origins of current assumptions about data. We then present examples of how racial categories can hide, rather than reveal, racial disparities. Finally, we apply the Model of Domain Learning to explain why data science and data management experts can and should expose experts in subject research to the idea of critically examining demographic data collection. Results: There are good reasons why patrons who are experts in topics other than racism can find it challenging to change habits from Interoperable approaches to race. Nevertheless, the Census categories explicitly say that they have no basis in research or science. Therefore, social justice requires that data librarians should expose researchers to this fact. If possible, data librarians should also consult on alternatives to habitual use of the Census racial categories. Conclusions: We suggest that many studies are harmed by including race and should remove it entirely. Those studies that are truly examining race should reflect on their research question and seek more relevant racial questions for data collection.
DeepPFCN: Deep Parallel Feature Consensus Network for Person Re-identification

Person re-identification aims to associate images of the same person over multiple non-overlapping camera views at different times. Depending on the human operator, manual re-identification in large camera networks is highly time consuming and erroneous. Automated person re-identification is required due to the extensive quantity of visual data produced by rapid inflation of large scale distributed multi-camera systems. The state-of-the-art works focus on learning and factorize person appearance features into latent discriminative factors at multiple semantic levels. We propose Deep Parallel Feature Consensus Network (DeepPFCN), a novel network architecture that learns multi-scale person appearance features using convolutional neural networks. This model factorizes the visual appearance of a person into latent discriminative factors at multiple semantic levels. Finally consensus is built. The feature representations learned by DeepPFCN are more robust for the person re-identification task, as we learn discriminative scale-specific features and maximize multi-scale feature fusion selections in multi-scale image inputs. We further exploit average and max pooling in separate scale for person-specific task to discriminate features globally and locally. We demonstrate the re-identification advantages of the proposed DeepPFCN model over the state-of-the-art re-identification methods on three benchmark datasets - Market1501, DukeMTMCreID, and CUHK03. We have achieved mAP results of 75.8%, 64.3%, and 52.6% respectively on these benchmark datasets.
A Pole Extraction Algorithm Based on Sliding Windowed Matrix Pencil Method

This paper proposes a pole extraction algorithm for robust radar target recognition based on Sliding Windowed Matrix Pencil Method (SW-MPM). The time domain scattering data of the target are windowed and the poles are extracted by Matrix Pencil Method (MPM) with a sliding window. The number of poles parameter is changed to improve the reliability of poles extraction, and the azimuth consistency of poles extracted from multiple azimuths is analyzed. We simulate and verify the effectiveness of the pole extraction method under different directions of the aircraft target.
Challenges and Responses: Subjects of Curriculum Implementation under Micro-Curriculum

The development of information technology is driving the advent of the micro era. In the context of the micro era, the micro curriculum brings many opportunities to the main subjects of the micro curriculum, namely teachers and students, but also brings many challenges. Explore the characteristics of the subject in the implementation of the micro-curriculum, clarify the relationship between the two major subjects, so that the subject can put forward effective strategies when responding to challenges. Because the teacher subject and the student subject stand at different angles of "teaching" and "learning" in teaching activities, they will face different challenges. Therefore, the subject of curriculum implementation starts from different angles, recognizes their respective challenges, and proposes corresponding effective coping strategies.
Improved Human Activity Recognition Model based on Sequence Mixup

In the practical application of smartphone-based human activity recognition (HAR), the location and orientation of smartphones are diverse and uncertain, which requires high robustness of deep learning model. Mixup is a novel regularization method, which has been successful in the field of image recognition, but it has the problem of amplitude information loss when applied to sensor data. The sequence mixup (S-mixup) is proposed, which replaces linear mixing in amplitude with interpolation in the time dimension. The proposed method can avoid the problem of amplitude information loss, stimulate the model to summarize the law of amplitude and orientation change, and extract features unrelated to smartphone orientation, to prevent overfitting. The experimental results show that the improved deep learning model based on the proposed method has high recognition accuracy when dealing with the adversarial examples with different smartphone orientations, which proves the effectiveness of the proposed method.
Automatic Classification of Modulation Schemes under Blind Scenario

Automatic modulation classification (AMC) is getting progressively significant in spectrum monitoring and cognitive radio. At the receiver side, both the identification and demodulation of signal rely upon the right modulation classification. The created acoustic channels are submerged, usually seen as one of the most irksome correspondence sources being utilized today. The spread of the acoustic network is best supported at low frequencies, and the information transmission available for correspondence is very con-stressed. Due to the multifaceted nature and feebleness of acoustic correspondence structures created submerged, it is difficult to perceive modification during real correspondence. In this paper, we have proposed a novel technique to classify four modulation schemes including BPSK, CPFSK, DSB-AM, and GFSK. The CPFSK and GFSK are classified for the first time with analog modulation. Firstly, spectrograms are formed from the signals, and features extracted from signals and RGB channels of spectrograms are then fused serially. Secondly, Analysis of variance was incorporated to diminish unnecessary features to enhance the system's computational efficiency. The system outcome successfully achieved an accuracy of 99.6% on the linear support vector machine (L-SVM).