Reviewerly/exHarmony huggingface dataset:
Monitoring and Assessment of the Effectiveness for Cultural Competence Development at University

This article is devoted to the research of the cultural competence development at the university level. It considers various interpretations of the concept of “cultural competence”, its componential criteria to assess students’ progress through their studying. The literature review revealed the redundancy of interpretations that can be explained by multiculturality of the concept as well as the lack of information, concerning assessment tools for the cultural competence monitoring. As a result of sifting and practical testing of a number of methods, the development of proprietary methodology eligible for the competencybased approach in the multi-level system of education is represented here. This study is intended to suggest compact and less time-consuming methods approbated by educators among the students of the Institute of Mathematics and Computer Science at the University of Tyumen, Russia.
System errors of manufacturers of medicines detected by conducting of state control in the area of circulation of medicines

The article conducts the analysis of system errors detected by Roszdravnadzor by conducting state quality control of circulating medicines, as well as weaknesses in pharmaceutical quality management systems of the manufacturers, that can influence the quality of manufactured drugs.
A Novel Approach to Find Author's Research Areas of Interests Using Graph Database

Research areas of interests reveal the area of expertise of an author or the field in which the author has in-depth knowledge. This is one of the core components of an author profiles of any academic search engine that can be efficiently utilized by other authors or researchers to identify all authors who have proficiency in a specified field. This article proposes a graph-based approach for automatic creation of author profile by finding the author's area of interests in research using subject classification of their published papers. Classification accuracy of the author's research areas of interest also tested manually for 415 authors by comparing classified areas of interests of each author with areas of interests given in Google scholar profile. Total mismatch occurs only for 37 others. Results showed that accuracy could be improved by adding more papers.
Utilization of the internet by undergraduate students of the University of Ibadan, Nigeria

The internet technology over the past few decades has become an important tool in higher education for learning, teaching and research.The study investigated the internet activities engaged in, frequency of use of the internet and challenges limiting the use of the internet among the undergraduate students of the University of Ibadan, Nigeria.A survey research design was adopted.A close-ended questionnaire was used to collect data from 300 undergraduate students.Findings were analysed using descriptive statistics and the result indicated that the respondents had good computer skills and used the internet for various activities which vary across faculty of study.The respondents were dissatisfied with the high cost of accessing the internet and inadequate internet facilities.The major recommendation of the study is the provision of basic internet training for the students; equipping the university with adequate internet facilities; regular power supply and free internet access to all university students.
At the crossroads

Having begun as a public services generalist the early 1990s, the electronic resources librarian position is most likely to be found in technical services units in the late 2010s (Stachokas, 2018). Even as contemporary academic libraries are redefined through the addition of new services, questions around electronic resources management are of ongoing importance, particularly in the deployment of personnel, tools, and best practices to provide increasingly electronic collections to users, but also in terms of the professional identity and skills of librarians. Since electronic resources account for the majority of scarce financial resources spent on collections and many academic libraries continue to spend roughly half of their budgets on collections, how these questions are resolved is of paramount importance to the survival and value of libraries. Improvements in analytics tools and techniques of assessment, as well as the broad conversation within the profession of library and information science, and between librarians, patrons, and stakeholders, will help guide libraries to make positive changes, but it is important to consider where we have been as well as where we are going.
Automated Identification of Breast Cancer Using Digitized Mammogram Images

In this paper, we present a new algorithm based on Computer-Aided Diagnosis (CAD) to detect breast cancer using digitized mammogram images. Here, we use image processing to make the pre-processing step of the images before we enter them into the classification step, in which we use machine learning for the classification of tissues in two conditions: normal and abnormal, either three conditions: normal, benign or malignant. In our CAD implementation, for pre-processing we are using transformations as binarization, thresholding, smoothing and the main operation, Gabor wavelet to suppress labels and unnecessary information, to obtain the best identifying characteristics. For feature selection and dimensionality reduction, we are using techniques as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (TSNE) and models of analysis of variance. Finally, for classification we discussed k-Nearest Neighbors (k-NN).
Experimental Analysis of Smart Green House with Rotary Garden

Abstract To overcome climate change that affects agriculture in all over the world, shows the estate and farming for the raising of the extreme weather (e.g., floods, droughts, etc ). Therefore, the smart greenhouse is essential in providing sustainable food for humans. The application of Smart Greenhouse, The ultimate and most common monitored variables are temperature, moisture, the intensity of sunlight and environmental conditions to support the using of sunlight a source of electricity. This is necessary for the Smart Greenhouse to manipulates the environment in appropriate to its plant’s needs. In this case, the temperature and humidity in the greenhouse are important to keeps the productivity of the plants. So, it’s using automatic system to keep suitable environmental conditions as explained before for the plants. In this research, the smart Greenhouses is shaped Ferris wheel to make sprinkling and radiation of sunlight for plants is equally distributed for all plants and its parts, Based on the results of this experiments with rotary mechanical system for Smart Greenhouse as well shown that Smart Greenhouse can preserve suitable temperature and moisture for growing the plants.
Note on the Benefit of Proof Representations by Name

In mathematical proofs axioms and intermediary results are often represented by their names. It is however undecidable whether such a description corresponds to an underlying proof. This implies that there is sometimes no recursive bound of the complexity of the simplest underlying proof in the complexity of the abstract proof description, i.e. the abstract proof description might be non-recursively simpler.
Analysis of Flood Vulnerability Using GIS Method (Case Study : Beringin Watershed, West Semarang)

Land degradation in the Beringin Watershed causes an increase of runoff and have high potential for flood disaster. The fact is proven by the distribution of land use for settlements in 1995 which was 18.45%, increased to 22.65% in 2002, thus enlarging the peak discharge in the Beringin watershed. To overcome this problem, it is necessary to study the effects of land use conversion on flood discharge and formulate the concept of management of Beringin watershed. Maximum annual daily rainfall be calculated with Polygon Thiessen method for 3 rain stations: Mijen, Mangkang Waduk, and Tugu for 20 years data (1998 – 2017). For the land use analysis, we was compared 2015 year Beringin watershed land use data with RTRW Semarang (Spatial Plans) 2011 – 2031 data and identified by using ArcGIS software. Whereas for the discharge design, the results of the HEC-HMS program modeling with 3 small dam was constructed for 10-years return period is 178.9 m3/dt. River normalization is design on the river downstream along ± 3.9 km, from the estuary (STA 0 + 6.2) to the Mangkang (STA 3 + 909.05) and from HEC-RAS simulation results shows that its can discharge safely.
URFD

Fatigue driving is a serious problem that probably causes a great damage to us. The fatigue driving action on the express way can be simply ruled according to the driving time, but it is ambiguous in the urban area and very hard to be detected. In this paper, we propose a reasonable definition of the fatigue driving action in urban area considering the driving time, driving speed, and driving area. According to the definition, we also design a distributed processing paradigm that can handle a massive volume of vehicle trajectories in real-time to detect the fatigue driving action. Finally, we conduct extensive experiments to evaluate the performance of our proposal based on a real data set released from DiDi.
Multi-Miner's Cooperative Evolution Method of Bitcoin Pool Based on Temporal Difference Leaning Method

Proof of Work (PoW) is used to provide a consensus mechanism for Bitcoin. In this mechanism, the process of generating a new block in the blockchain is referred to as mining. Such process is intentionally designed to be resource-intensive and time consuming so that the rate of block generation remains steady. A single participant, called a miner usually has limited computation power to produce PoWs. This leads miners to form a mining pool, where miners aggregate their computing power and share the rewards. However, a phenomenon raises in such a mining pool activity, where miners attack each other. Consequently, this results in a decrease in total rewards received from the mining pool. To address the abovementioned problem, we build a multi-miner model for forming a mining pool. We further propose a method to improve the cooperation-probability of miners in the pool by introducing a Zero-Determinant strategy and a Temporal Difference learning method (TD(λ)). Experimental simulation results show that the proposed method can effectively promote the cooperation among miners, therefore, increase the rewards received from the formed mining pool.
Fine-grained Cross-media Representation Learning with Deep Quantization Attention Network

Cross-media search is useful for getting more comprehensive and richer information about social network hot topics or events. To solve the problems of feature heterogeneity and semantic gap of different media data, existing deep cross-media quantization technology provides an efficient and effective solution for cross-media common semantic representation learning. However, due to the fact that social network data often exhibits semantic sparsity, diversity, and contains a lot of noise, the performance of existing cross-media search methods often degrades. To address the above issue, this paper proposes a novel fine-grained cross-media representation learning model with deep quantization attention network for social network cross-media search (CMSL). First, we construct the image-word semantic correlation graph, and perform deep random walks on the graph to realize semantic expansion and semantic embedding learning, which can discover some potential semantic correlations between images and words. Then, in order to discover more fine-grained cross-media semantic correlations, a multi-scale fine-grained cross-media semantic correlation learning method that combines global and local saliency semantic similarity is proposed. Third, the fine-grained cross-media representation, cross-media semantic correlations and binary quantization code are jointly learned by a unified deep quantization attention network, which can preserve both inter-media correlations and intra-media similarities, by minimizing both cross-media correlation loss and binary quantization loss. Experimental results demonstrate that CMSL can generate high-quality cross-media common semantic representation, which yields state-of-the-art cross-media search performance on two benchmark datasets, NUS-WIDE and MIR-Flickr 25k.
Nonparametric Bayesian machine learning and signal processing

Abstract We have seen that stochastic processes play an important foundational role in a wide range of methods in DSP. For example, we treat a discrete-time signal as a Gaussian process, and thereby obtain many mathematically simplified algorithms, particularly based on the power spectral density. At the same time, in machine learning, it has generally been observed that nonparametric methods outperform parametric methods in terms of predictive accuracy since they can adapt to data with arbitrary complexity. However, these techniques are not Bayesian so we are unable to do important inferential procedures such as draw samples from the underlying probabilistic model or compute posterior confidence intervals. But, Bayesian models are often only mathematically tractable if parametric, with the corresponding loss of predictive accuracy. An alternative, discussed in this section, is to extend the mathematical tractability of stochastic processes to Bayesian methods. This leads to so-called Bayesian nonparametrics exemplified by techniques such as Gaussian process regression and Dirichlet process mixture modelling that have been shown to be extremely useful in practical DSP and machine learning applications.
Network Forensic Investigation in OpenContrail Environments

The requirements of today's data center networks include scalability, multi-tenancy and isolation from the underlying infrastructure, which are primarily achieved through the use of network virtualization. As a downside, the overall complexity increases with the number of technologies involved, which has a significant impact upon network forensic investigation. In this context we investigated OpenContrail, an open source framework for network virtualization that provides built-in methods for collecting network traffic. In our research, we concluded that these methods work in principle, but are not suitable to capture network traffic that can be used in court. The packet mirroring turned out to be incomplete and the capture process can be detected by the virtual machine under investigation. Based on these findings, we developed a more flexible agent that especially ensures the transparency of the capture process for the suspicious virtual machine.
Heavy metals in well’s waters within Southern Basrah Province / Iraq.

Abstract Due to continuous decline of water discharge in southern Iraq, the need arises to use ground water as alternate source for water required for different purposes in which it suppose to be clean and free of pollutants such as, petroleum hydrocarbons, heavy metals, pesticides, …, etc. Within this study, ten well’s of water spread in Southern part of Iraq, two in each site represented by Safwan (1 and 2), Zubair (3 and 4), Khor Al-Zubair (5 and 6), Al-Burjisiah (7 and 8), and Shuaibah (9 and 10) were investigated to estimate their contents of heavy metals. The collected water samples from the selected well’s were analyzed for total heavy metals: Cd, Cu, Fe, Mn, Ni, Pb, and Zn, on a monthly basis for the period Jan. - Dec. 2015. Atomic absorption spectroscopy technique was adopted for the determination of heavy metal concentrations. All studied metals are present in the water of wells’ spread in Southern part of Basrah Province of Iraq. Certain metals present in little amounts (µg/l) in the range 0 - 0.5649 for Pb, 0 - 4.3909 for Ni, 0 - 32.0170 for Cu, and 0 - 8.5486 for Cd, while other metals were higher in the range 0 - 107.9698 for Mn, 4.336 - 312.1651 for Fe, and 11.982 - 439.3581 for Zn. Fe and Zn are exist in all wells’ and during all periods of study. As a mean values the trends of heavy metals in well’ water of southern Iraq is as follows: Zn&gt;Fe&gt;Mn&gt;Cd&gt;Cu&gt;Ni&gt;Pb. High concentrations of studied heavy metals were recorded in wells’ no. 7&amp;8 at Burjisiah and 9 &amp;10 at Shuaibah due to proximity to fields of oil production and transportation. Moreover, high levels of heavy metals in the wells’ waters at studied, area were recorded during the rainy months: Jan., Feb., Mar., April as well as May and June which indicates that rain water is a major source for pollution by heavy metals in the ground water at Southern Iraq.
Incremental Learning of People Identities

Face recognition in unconstrained open-world settings is a challenging problem. Differently from the closed-set and open-set face recognition scenarios that assume that the face representations of known subjects have been manually enrolled in a gallery, the open-world scenario requires that the system learns identities incrementally from frame to frame, discriminate between known and unknown identities and automatically enrolls every new identity in the gallery, so to be able to recognize it every time it is observed again in the future. Performance scaling with large number of identities is likely to be needed in real situations. In this paper we discuss the problem and present a system that has been designed to perform effective open-world face recognition in real time at both small-moderate and large scale.
Early Anomalous Vehicular Traffic Detection Through Spectral Techniques and Unsupervised Learning Models

Smart Mobility seeks to meet urban requirements within a city and solve the urban mobility problems, one of them is related with vehicular traffic. The anomalous vehicular traffic is an unexpected change in the day-to-day vehicular traffic caused by different reasons, such as an accident, an event, road works or a natural disaster. An early detection of anomalous vehicular traffic allows to alert drivers of the anomaly and can make better decisions during their journey. The current solutions for this problem are mainly focused on the development of new algorithms, without giving enough importance to the extraction of underlying information from vehicular traffic, and even more, when this is a univariate time series and it is not possible to obtain other context features that describes its behavior. To address this issue, we propose a methodology for temporary, spectral and aggregation features and an unsupervised learning model to detect anomalous vehicular traffic. The methodology was evaluated in a real vehicular traffic database. Experimental results show that by using spectral attributes the detection of anomalous vehicular traffic, the Isolation Forest obtains the best results.
Nonlinear and non-Gaussian signal processing

Abstract Linear, time-invariant (LTI) Gaussian DSP, has substantial mathematical conveniences that make it valuable in practical DSP applications and machine learning. When the signal really is generated by such an LTI-Gaussian model then this kind of processing is optimal from a statistical point of view. However, there are substantial limitations to the use of these techniques when we cannot guarantee that the assumptions of linearity, time-invariance and Gaussianity hold. In particular, signals that exhibit jumps or significant non-Gaussian outliers cause substantial adverse effects such as Gibb's phenomena in LTI filter outputs, and nonstationary signals cannot be compactly represented in the Fourier domain. In practice, many real signals show such phenomena to a greater or lesser degree, so it is important to have a `toolkit' of DSP methods that are effective in many situations. This chapter is dedicated to exploring the use of the statistical machine learning concepts in DSP.
Diffraction by a Truncated Slab Lying on a PEC Plate

Open dielectric waveguides are important especially in the areas of integrated optics and millimeter-wave circuits. For the study of these geometries, a variety of analytical methods have been proposed in the past. In particular the approaches that make use of the Wiener Hopf (WH) technique are very rigorous [1]. In this paper the Fredholm factorization of the Wiener Hopf equations is [1]-[2] extended to the study of the diffraction by a plane wave by a semi-infinite dielectric slab.
Boosted Trees Model with Feature Engineering

The post-click conversion rate (CVR) prediction is important for the sponsored search engine. The predicted CVR can guide people on how to choose the advertisements to display, and greatly affects the revenue of the electronic business platform and merchants who pay for the advertisement. In this paper, we describe a new approach to solve the CVR prediction problem in sponsored search. The approach is Boosted Trees Model with Feature Engineering (BTMFE), which incorporates the power of feature engineering as well as the boosted trees. We discuss different issues such as feature importance, feature interaction, historical information fusion and boosted trees algorithm. Finally, we compare BTMFE with other methods and present experimental results on real-world datasets.
Operations over Linear Secret Sharing Schemes

A secret sharing scheme implemented in an organisation is designed to reflect the power structure in that organisation. When two organisations merge, this usually requires a number of substantial changes and, in particular, changes to their secret sharing schemes which have to be merged in the way which reflects a new role of each of the organisations. This paper looks at the ways secret sharing scheme can be modified when organisational changes occur. We restrict ourselves with the class of ideal linear secret sharing schemes and describe how matrices of these linear schemes have to be modified when we take the sum, the product or the composition of two linear access structures.
Differential Study of Deep Network Based Image Retrieval Methods

Learning successful component portrayals and likeness measures are essential to the recovery execution of a substance based image recovery (CBIR) framework. In spite of broad research endeavors for quite a long time, it stays one of the most testing open issues that extensively thwarts the accomplishments of genuine world CBIR frameworks. The key test has been ascribed to the outstanding “semantic hole” issue that exists between low-level image pixels caught by machines and elevated level semantic ideas saw by a human. Among different methods, AI has been effectively examined as a conceivable course to connect the semantic hole in the long haul. Propelled by late triumphs of profound learning strategies for PC vision and different applications, in this paper, we endeavor to address an open issue: if profound learning is a desire for spanning the semantic hole in CBIR and how much upgrades in CBIR undertakings can be accomplished by investigating the cutting edge profound learning procedures for learning highlight portrayals and comparability measures. In particular, we research a structure of profound learning with application to CBIR assignments with a broad arrangement of exact investigations by inspecting cutting edge profound learning strategies for CBIR undertakings under shifted settings. From our experimental investigations, we locate some promising outcomes and abridge some significant bits of knowledge for future research.
Runtime analysis of discrete particle swarm optimization algorithms: A survey

Abstract A discrete particle swarm optimization (PSO) algorithm is a randomized search heuristic for discrete optimization problems. A fundamental question about randomized search heuristics is how long it takes, in expectation, until an optimal solution is found. We give an overview of recent developments related to this question for discrete PSO algorithms. In particular, we give a comparison of known upper and lower bounds of expected runtimes and briefly discuss the techniques used to obtain these bounds.