Reviewerly/exHarmony huggingface dataset:
Heavy metals contents in higher aquatic plants of Belarusian reservoirs and rivers: spatial and species features

The spatial and species features of heavy metals (Ti, V, Cr, Mn, Ni, Cu, Zn, Zr, Mo, Pb) contents in higher aquatic plants of reservoirs and rivers within Belarus have been analyzed on monitoring data. The group of submerged plants are characterized by high ash content; and they accumulate large amounts of Ti, Mn, Cu, Zn, Zr, Pb. There are statistically signi ficant differences between the plants in ecological groups of reservoirs and rivers in terms of Mn, Cu, Pb contents in the tissues (elements with a 100 % frequency). According to the spatial analysis the reservoirs of the northern geochemical province account for 71 % of the Mn anomalies and 80 % of the Cu anomalies, including the Braslav region – 46 % of the Mn anomalies and 59 % of Cu. Mn, Cu, Pb maximum concentrations are characteristics of submerged macrophytes such lakes as Snudy, Losvido, Maloye Beloye, Beloye (Luninets District), Beloe (Surmino), Svityaz. Among submerged macrophytes within lakes the abnormal accumulation of Mn has been noted for Charophyta (61 % of cases), Myriophyllum and Ceratophyllum, Cu – Charophyta, Isoetes and Lobelia dortmanna, Pb – Isoetes, Charophyta and Potamogeton. The macrophytes within rivers are characterized by the higher ash content and the average content of Mn, Cu and Pb in comparison with the lakes plants. Areas with identified Cu and Pb anomalies in the submerged plants are confined to the central geochemical province of Belarus (the Svisloch river accounts for 67 % of anomalous values of Cu and 33 % of Pb). Anomalous values of Cu have been fixed in the tissues of the Ceratophyllum and Potamogeton, Pb – mostly in Potamogeton. The cluster analysis has distinguished a group of plant species as a part of the following genera: Potamogeton, Myriophyllum, Elodea, Ceratophyllum and Sparganium. The group is characterized by elevated contents of Ti, V, Cr, Mn, Ni, Cu, Zn, Zr, Pb and recommended as indicators of polluting water objects with heavy metals.
Taming Complexity with Self-managed Systems

Modern computer information systems are highly complex, networked, have numerous configuration knobs, and operate in environments that are highly dynamic and evolving. Therefore, one cannot expect that configurations established at design-time will meet QoS and other non-functional goals at run-time. For that reason, the design of complex systems needs to incorporate controllers for adapting the system at run time. In this talk I will describe the four properties of self-managed systems: self-configuring, self-optimizing, self-healing, and self-protecting. I will also describe how these properties are enforced by controllers I designed for a variety of domains including cloud computing, fog/cloud computing, internet datacenters, distributed software systems, and database systems.
New approach for attack of permutation-based image encryption schemes

Cryptanalysis encloses a set of tools that detects ciphers weakness and reconstructs the related plain shape. It leads designers to evaluate and perform encryption systems. In the literature, most of the works that meet this requirement try to recover the complete plain form of a given cipher, which needs exhaustively time and resources and thus, unlike to the cryptanalysis real goal which restricts to the acquisition of just the cipher sense and its main idea. Thus the present approach is built upon such concept. It tries to recover the image means by considering reduced search space elements. We demonstrate the usefulness of the approach based on the results of various tests in regard with similar literature works.
Quantitative Determination of Gas Phase Composition of Fluid Inclusions in Quartz from Krasnoye Gold Deposit (the Eastern Siberia) by Raman Microspectroscopy

The analysis of the gas phase composition of fluid inclusions in quartz from Krasnoye gold deposit (Eastern Siberia) was carried out using Horiba LabRam HR800 Evolution Raman spectrometer. CO2 was found to be the major component of fluid inclusions in quartz; it is formed due to fluid-rock interaction during the destruction of host black shales. CO2 mol fraction (and CO2 density) in inclusions in quartz from the gold-bearing and barren vein were estimated as 96.9–98.6 and 88.3–98.0% mol (1.23–1.29 and 1.25–1.30 g/cm3), respectively. No differences in CO2 and N2 concentrations were found between the gold-bearing and barren quartz veins. The calculation results of the gas phase composition have been confirmed by gas chromatography. Based on our experience, study the composition of fluid inclusions which size is in the range 5–40 μm and located at the depth of about 100 μm from the surface of transparent minerals is promising when using HR800 Raman spectrometer.
Implementation of Dalal and Triggs Algorithm to Detect and Track Human and Non-Human Classifications by Using Histogram-Oriented Gradient Approach

In this paper, the proposed application is mainly used for security purpose and it deals with searching based on the given input and integrates this approach with the concept of histogram of oriented gradient (HOG) features to achieve person or non-person classification in human tracking system. It captures the features of human automatically based on the gradients of an image in human tracking system. This tracking system process will be done only through the expert team. Before getting into the process, the images or videos which have been captured from the surveillance camera have to be uploaded and checked whether it is a human being or not. Two leading approaches to the application are: This application process is done based on some scenarios like color code, distance between the object and the camera, and the height of the human being.
Image Restoration Network with Adaptive Channel Attention Modules for Combined Distortions

The image obtained from systems such as autonomous driving cars or fire-fighting robots often suffer from several degradation such as noise, motion blur, and compression artifact due to multiple factor.It is difficult to apply image recognition to these degraded images, then the image restoration is essential.However, these systems cannot recognize what kind of degradation and thus there are difficulty restoring the images.In this paper, we propose the deep neural network, which restore natural images from images degraded in several ways such as noise, blur and JPEG compression in situations where the distortion applied to images is not recognized.We adopt the channel attention modules and skip connections in the proposed method, which makes the network focus on valuable information to image restoration.The proposed method is simpler to train than other methods, and experimental results show that the proposed method outperforms existing state-of-the-art methods.
Bayesian networks: The minimal triangulations of a graph

Graph triangulation plays an essential role in the study of the Bayesian networks, especially in the celebrated Junction Tree Algorithm. Here we present a simple new proof for CompMLSM by Berry et al. (2009), which generalizes all known algorithms searching for the minimal triangulations of a graph. We also show how CompMLSM may be implemented in our General Minimal Triangulation algorithm.
Research on Task Pricing of Self-service Platform of Product-Service System

The realization of mobile Internet self-service platform provides product maintenance in product-service system of enterprises with more diversified and efficient market research methods. This paper combines service pricing theory, cost-oriented pricing theory and competition-oriented pricing theory, through market data analysis, discusses the influencing factors of the task pricing of product maintenance in product-service system, and establishes a pricing model to analyze the influencing factors of pricing.
ME3CA - Monitoring Environment Exercise and Emotion by a Cognitive Assistant

The elderly population has increased dramatically in today’s society. This fact implies the need to propose new policies of attention to this group but without increasing social spending. Currently, there is a need to promote the care of elderly people in their own homes, avoiding being transferred to saturated residences. Bearing this in mind, in recent years numerous approaches have tried to offer solutions in this sense using the continuous advances in new information and communication technologies. In this way, this article proposes the employment of a personal assistant to help the elderly in the development of their daily life activities. The proposed system, called ME3CA, is a cognitive assistant that involves users in rehabilitating exercise, consisting of a sensorization platform and different integrated decision-making mechanisms. The system tries to plan and recommend activities to older people trying to improve their physical activity. In addition, in the decision making process the assistant takes into account the emotions of the user. In this way, the system is more personalized and emotionally intelligent.
THE IMPACT OF LEADERSHIP STYLES ON R&D TEAM PERFORMANCE AND THE MEDIATING ROLE OF KNOWLEDGE SHARING

Purpose: This study aims to examine the impact of leadership styles, specifically on transformational leadership and transactional leadership in R&amp;D team performance of researcher in UniversitiTeknikal Malaysia Melaka (UTeM). Further, this study was to examine the impact of knowledge sharing on R&amp;D team performance as mediating variables. Methodology: The survey method was adopted to carry out the research. A structured Multifactor Leadership Questionnaire (MLQ) was used for data collection. R&amp;D team performance was assessed based on measures of performance. Regression and Correlation statistical techniques were used to analyze the data elicited from one hundred seventy two (172) randomly selected respondents. Main Findings: It was revealed from the study that while transformational leadership style had a positive impact on R&amp;D team performance; transactional leadership style also had a positive impact on R&amp;D team performance. It was also revealed from the study that knowledge sharing have significant and positive relationship with mediate both transformational leadership and transactional leadership on R&amp;D team performance. Implications/Applications: This research shows that R&amp;D teams also can use the full range leadership styles although there are comparisons of uniqueness that exists in the R&amp;D teams with other contexts. This is because leading an R&amp;D team requires the leader to possess certain skills in addition to technical expertise, such as spreading information regarding technical advances, being knowledgeable regarding current professional activities and possessing strategic planning skills in an innovative climate where the team is not permanent or lasts only for a short period of time. This research confirmed that leadership styles led to significant team performance benefits such as an increase achievement of technical objectives, efficiency of resource utilization and other achievements. Novelty/Originality: This study has successfully extended the full leadership literature by conceptualizing how the transformational leadership and transactional leadership styles positively impact the R&amp;D team's performance. Hence, the findings in this study may be beneficial and act as a framework or a limitation for other studies.
A Robust Tracking with Low-Dimensional Target-Specific Feature Extraction

Pre-trained CNNs on ImageNet have been widely used in object tracking for feature extraction.However, due to the domain mismatch between image classification and object tracking, the submergence of the target-specific features by noise largely decreases the expression ability of the convolutional features, resulting in an inefficient tracking.In this paper, we propose a robust tracking algorithm with low-dimensional targetspecific feature extraction.First, a novel cascaded PCA module is proposed to have an explicit extraction of the low-dimensional target-specific features, which makes the new appearance model more effective and efficient.Next, a fast particle filter process is raised to further accelerate the whole tracking pipeline by sharing convolutional computation with a ROI-Align layer.Moreover, a classification-score guided scheme is used to update the appearance model for adapting to target variations while at the same time avoiding the model drift that caused by the object occlusion.Experimental results on OTB100 and Temple Color128 show that, the proposed algorithm has achieved a superior performance among real-time trackers.Besides, our algorithm is competitive with the state-of-the-art trackers in precision while runs at a real-time speed.
Engaging a Data Revolution: Open Science Data Hubs and the New Role for Universities in Africa

Abstract This paper presents a new ideology for engaging Africa in a data revolution. It explores the idea of creating open-science-data-hubs (OSDH) at flag-ship universities in Africa to preserve and share both internally and externally produced data. Although limited in the technical aspect, the objective here is to explore the pragmatism of how and why such an endeavor in Africa should be undertaken. This paper argues that the African university is uniquely placed to play this new role in today’s technological world and discusses the characteristics and foundational pillars necessary to set up such a program. The arguments provided here challenge Africa to be smart and adopt clever solutions to their data generation, collection and access problems, by finding value and a new role in the intellectuals and institutions of higher learning and in the necessity to involve them in the generation, preservation and sharing of data and knowledge that can be used in the policy formulation process.
A deep linguistic computer-assisted language learning game for Italian

T his paper describes the initial stages of a project which seeks to develop a language learning game for Italian running a deep linguistic grammar at its backend for fine-grained error detection.The grammar is designed within the grammatical framework of Lexical Functional Grammar (LFG).The project aims to bring together work from different fields by combining strategies from computational linguistics with theoretical insights from Second Language Acquisition (SLA) and components from computer gaming.
Subjective Effects of Flying With an Autoscopic Self-Avatar in Third-Person Perspective Within an Immersive Mixed Reality

This paper details the development and use of a scientific experimental apparatus providing participants with an immersive experience of flight by means of a head-mounted display showing them hovering in a virtual world. It aims to understand the technological and human mechanisms at work when users identify themselves with a self-avatar perceived from a third-person perspective. The design principles that made it possible to manufacture this device are explained for each aspect. Then, two sets of raw results obtained by questionnaires or interviews will be presented, concerning respectively 43 test subjects in a controlled setting and 91 festival goers, in order to show how the methodology and the gathered data have confirmed the capacity of our experience to highlight different adaptation strategies in the face of one's self-avatar and the effects of bilocation.
An Improved Capsule Network Based on Newly Reconstructed Network and the Method of Sharing Parameters

The capsule network is considered as the latest technology in the field of computer vision. However, it needs a large amount of storage space due to the large amount of parameters. In this paper, we have adopted two methods to solve this problem. First, a method of sharing the parameters of capsule layer is proposed to solve the problem of too many parameters in capsule layer, which can decrease by 18% parameters compared with the previous. Second, we redesigned the structure of the reconstructed network to replace the original, reducing the network’s parameters by 16%. Moreover, we combine the two methods to further reduce the parameters, which can decrease by 34%. Finally, we use the improved capsule network for MNIST handwritten digit recognition, the result is almost the same as or even slightly higher than the original capsule network, and the reconstructed images also can smooth the noise. This article provides new ideas for the future optimization methods of various capsule networks.
Succinct Predicate and Online-Offline Multi-Input Inner Product Encryptions under Standard Static Assumptions

This paper presents expressive predicate encryption (PE) systems, namely non-zero inner-product-predicate encryption (NIPPE) and attribute-based encryption (ABE) supporting monotone span programs achieving best known parameters among existing similar schemes under well-studied static complexity assumptions. Both the constructions are built in composite order bilinear group setting and involve only 2 group elements in the ciphertexts. More interestingly, our NIPPE scheme, which additionally features only 1 group element in the decryption keys, is the first to attain succinct ciphertexts and decryption keys simultaneously. For proving selective security of these constructions under the Subgroup Decision assumptions, which are the most standard static assumptions in composite order bilinear group setting, we apply the extended version of the elegant Déjà Q framework, which was originally proposed as a general technique for reducing the q-type complexity assumptions to their static counter parts. Our work thus demonstrates the power of this framework in overcoming the need of q-type assumptions, which are vulnerable to serious practical attacks, for deriving security of highly expressive PE systems with compact parameters. We further introduce the concept of online-offline multi-input functional encryption (OO-MIFE), which is a crucial advancement towards realizing this highly promising but computationally intensive cryptographic primitive in resource bounded and power constrained devices. We also instantiate our notion of OO-MIFE by constructing such a scheme for the multi-input analog of the inner product functionality, which has a wide range of application in practice. Our OO-MIFE scheme for multi-input inner products is built in asymmetric bilinear groups of prime order and is proven selectively secure under the well-studied k-Linear (k-LIN) assumption.
Lemmatization of big data in the Kazakh language

In paper are considered existing algorithms for automatically isolating the bases for a number of natural languages and possible ways of synthesizing a normal form of a word for the Kazakh language. In paper are described the complete system of endings of the Kazakh language. The article presents the classification of affixes Kazakh language. The paper proposes a new approach to constructing a lemmatization algorithm for the Kazakh language on the basis of a complete set of endings of the Kazakh language. The lemmatization algorithm will be used for Kazakh language information retrieval to finding specific words in the documents by stemming base of word.
Application of Improved Critical Chain and Earned Value Technique in Software Engineering &amp; Integrated Project Progress Control

Software engineering & Integrated project includes both software development and hardware construction processes ,while its progress control is one of the main problems faced by construction units and owners. Critical chain project management minimized the risk of progress through setting a Buffer, managed stratification of the software and hardware processes of the software engineering and integrated project, compared with traditional progress management methods , which owned natural advantages. Based on the critical chain management methods, this paper proposed an improved technique which combined critical chain with the traditional earned value technique. Drawn references from the PERT Methods, this method determined the scheduled duration in three situations: unconsumed buffer, completely consumed buffer and most likely consumed buffer, transformed the critical chain project duration into traditional earned value project duration, and then carried out the earned value analysis, at the same time, the monitoring of buffer consumption was improved. The method controlled the project schedule at both external and internal levels, which improved the accuracy and flexibility of duration monitoring and conducted an empirical research by taking a software engineering and integrated project as an example.
Cuscus: An End User Programming Tool for Data Visualisation

We present Cuscus, a tool for data visualisation that is informed by ethnographic fieldwork across different professional sectors. Cuscus allows end-users to create novel visualisations by defining visual properties in a spreadsheet. We also report on user studies in the contexts of data journalism and business analytics, and discuss further extensions to this new interaction paradigm.
Examination on the use of emoticon technique as a communication tool for narrative skills

The aim of this study is to examine how critically and creatively the students express their feelings and thoughts by using emoticons.In the first part, how the students expressed what they read by using emoticons was examined.In order to assess their skills for reading comprehension and the skills for expressing what they understood through emoticons, an achievement test was applied.The second part was reserved for the case study.The case analysis processes (items) were designed based on the case.In the end of the pilot application, both the achievement test and the case analysis processes (items) of the case were reviewed and completed.In actual application the findings suggested that the students were able to comprehend what they read and express their emotions and ideas in a certain context in a critical and creative way by using emoticons.Based on this finding, it has been suggested that emoticons used in many parts of the world should take place consciously in education.
Using Non-Functional Requirements to Identify Cross-Cutting Concerns

Smart Home systems control a multitude of automated devices and bring together different types of stakeholders. From software perspective, this rises architecture and design challenges, as different stakeholder viewpoints are sources of different requirements. Apart from the functional requirements available in the initial product description, non-functional requirements and constrains are difficult to capture, and in addition, they are the main generator of cross-cutting requirements. The process presented here identifies cross-cutting concerns, as architectural aspects cross-cut quality attributes. Using a metric, we calculate cross-cutting coefficients, based on matching values mapped from non-functional requirements. The process generates a space cross-cutting matrix, which quantifies cross-cutting coefficients for architectural aspects and system quality attributes. The information generated about the cross-cutting concerns can be used in planning the software and system development, and in decisions related to the system architecture, design patterns.
Text Entry Performance on an Expandable Socket Attached Smartphone in Stationary and Mobile Settings

This paper investigates the effects of an expandable socket attachment for mobile phone grip on stationary (while seated) and mobile (while walking on a treadmill) text entry. An exploratory study (N = 12) failed to identify a significant effect of the attachment on text entry speed, accuracy, and error correction effort in either settings. But participants performed relatively better with the attachment while walking. Participants also did not perceive the attachment to affect their text entry speed and accuracy. However, significantly more participants wanted to use it in mobile settings, presumably to increase the safety of their devices.