Reviewerly/exHarmony huggingface dataset:
Authentic Leadership in Determining Employee’s Organizational Commitment: The Mediating Role of Knowledge Sharing and Organizational Virtuousness

This study is conducted to explore the role of authentic leadership on organizational commitment of employees. Knowledge sharing and organizational virtuousness are used as mediating variable in the study. Banking industry is selected for collection of data and result implications. The data was collected using the standard adopted questionnaire for each variable from branches of five major Pakistan’s banks. We applied Pearson correlation, regression and hierarchal analysis for pragmatic purposes. Our outcomes point out that all variables have significant positive correlation among all variables of the study. Authentic leadership impact on organizational commitment found significant. Moreover, authentic leadership impact on knowledge sharing and organizational commitment is also found significant. The impact of organizational virtuousness on organizational commitment is significant but the impact of knowledge sharing on organizational commitment is found insignificant. With respect to mediating results both are found to be significant. So we can infer that banks industry put attention towards these variables to enhance their employees performance and improve working environment and ultimately to succeed in their objectives in these competitive corporate environment.
A Character Frequency based Approach to Search for Substrings of a Circular Pattern and its Conjugates in an Online Text

A fundamental problem in computational biology is to deal with circular patterns. The problem consists of finding the least certain length substrings of a pattern and its rotations in the database. In this paper, a novel method is presented to deal with circular patterns. The problem is solved using two incremental steps. First, an algorithm is provided that reports all substrings of a given linear pattern in an online text. Next, without losing efficiency, the algorithm is extended to process all circular rotations of the pattern. For a given pattern P of size M, and a text T of size N, the algorithm reports all locations in the text where a substring of Pc is found, where Pc is one of the rotations of P. For an alphabet size σ, using O(M) space, desired goals are achieved in an average O(MN/σ) time, which is O(N) for all patterns of length M ≤ σ. Traditional string processing algorithms make use of advanced data structures such as suffix trees and automaton. We show that basic data structures such as arrays can be used in the text processing algorithms without compromising the efficiency.
Ant Colony-Based K-Mediod for Data Clustering

The paper presents a K-mediod clustering algorithm based on ant colony optimization theory. Finding clusters in data is a challenging problem. Clustering task aims at the unsupervised classification of patterns in different clusters. Clustering with ant-based algorithm is the most promising method and has been shown to produce good results in a wide variety of problems. With this motivation, the paper proposes a novel ant-based K-mediods algorithm that modifies the ant K-means as locating the objects in a cluster with the probability, which is updated by the pheromone, while the rule of updating pheromone is according to the value, which is defined as the ratio of the total variation and within cluster variation.An experiment is conducted on both artificial and real-life databases. The experimental outcome confirms that clusters obtained through ant-based K-mediods are significantly better than clusters obtained through K-means and ant K-means in all databases.
Analysis of the evolution of culture

The paper is devoted to the analysis of determinants and sources of culture. If earlier certain cultural values have not changed for many centuries, determining the existence of more than one generation, now several cultural eras alternate throughout one life. The transition to a post-industrial society leads to a transformation in the spiritual sphere and the emergence of mass culture. It is shown that the starting point for the philosophical reflection of mass culture should be beyond the consideration of this phenomenon only as a phenomenon of artistic life of "people of mass culture" and the definition of its conditionality from the development of culture of society as a whole.
DEVELOPMENT OF BIOMETRIC METHODS AND INFORMATION SECURITY TOOLS

Now a day’s security is a big issue, the whole world has been working on the face recognition techniques as face is used for the extraction of facial features. An analysis has been done of the commonly used face recognition techniques. This paper presents a system for the recognition of face for identification and verification purposes by using Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) and the implementation of face recognition system is done by using neural network. The use of neural network is to produce an output pattern from input pattern. This system for facial recognition is implemented in MATLAB using neural networks toolbox. Back propagation Neural Network is multi-layered network in which weights are fixed but adjustment of weights can be done on the basis of sigmoidal function. This algorithm is a learning algorithm to train input and output data set. It also calculates how the error changes when weights are increased or decreased. This paper consists of background and future perspective of face recognition techniques and how these techniques can be improved.
Modern technical and forensic tools used in disclosure crimes

The article presents modern methods of using technical and forensic tools to investigating offenses.In particular, the application of technical equipment for inspection, recording and removal of traces at the scene of the accident is described.In addition, attention is paid to modern tools used in the investigation of crimes.
Automatic Book Rack System with Expandable Clasp Holder: A Short Review

In the light of number of universities and libraries consistently increasing and book storage/ maintenance becoming a difficult task, there is a need to design a system which will reduce manual work as well as minimise the problem of using the book racks. The review is done to identify the various shortcomings of the present system and overcome the flaws faced such as accessing books at height, cleaning the shelves at the topmost compartment and holding the books upright in proper position. The automatic book rack system includes shelf, expandable clasp housing that is movable and connected to the shelf. The housing includes a noiseless electric motor and a pair of chain and sprockets. The electric motor, chain sprockets work in conjunction to achieve linear motion of the housing along the shelf. The expandable clasp can support the books in an upright, side by side arrangement on the shelf. Once a book is removed from the shelf the clasp housing is automatically repositioned to push the remaining books together and close any resulting gaps between the remaining books keeping them upright. Exemplary systems according to present disclosure include the mechanism that permit reliable and efficient repositioning of one or more shelves, thereby enhancing utilization and efficiencies associated therewith. For completion of this task we have successfully designed the automatic book rack and with suitable material selection the ABR system has been fabricated.
DW-GAN: A Discrete Wavelet Transform GAN for NonHomogeneous Dehazing

Hazy images are often subject to color distortion, blurring, and other visible quality degradation. Some existing CNN-based methods have great performance on removing homogeneous haze, but they are not robust in non-homogeneous case. The reasons are mainly in two folds. Firstly, due to the complicated haze distribution, texture details are easy to be lost during the dehazing process. Secondly, since the training pairs are hard to be collected, training on limited data can easily lead to over-fitting problem. To tackle these two issues, we introduce a novel dehazing network using 2D discrete wavelet transform, namely DW-GAN. Specifically, we propose a two-branch network to deal with the aforementioned problems. By utilizing wavelet transform in DWT branch, our proposed method can retain more high-frequency knowledge in feature maps. In order to prevent over-fitting, ImageNet pre-trained Res2Net is adopted in the knowledge adaptation branch. Owing to the robust feature representations of ImageNet pre-training, the generalization ability of our network is improved dramatically. Finally, a patch-based discriminator is used to reduce artifacts of the restored images. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-arts quantitatively and qualitatively.
Satellite image processing based on percolation for physicochemical analysis of soil cover of industrial waste facilities

Remote sensing of the Earth allows receiving medium, high spatial resolution, and hyperspectral measurements from spacecraft. This study presents a remote sensing application of using time-series satellite images for monitoring solid waste disposal facilities (WDF). We proposed a method for satellite image processing using the percolation for physicochemical analysis of soil cover of industrial waste facilities. This work aims to study different methods for assessing percolation parameters from space images. The article discusses ways of fractal-percolation, chemical, and regression analysis. The proposed algorithm results are shown on the example of the solid household and the industrial waste landfill. The received results can serve as the basis for developing a methodology for assessing the effectiveness of measures to neutralize the underlying surface of the WDF against the filtrate and seep it into the soil using remote sensing technologies of Earth.
Making Prints: How Learning Printmaking During Pandemic Can Boost Students’ Creativity

Initially, this research was based in the idea of combining online and offline classroom materials to teach art (Blended Learning).But since the start of 2020, learning everywhere have been pushed towards non-physical because of pandemic.Challenges in teaching art practices becomes visible and emphasised.Although Blended Learning method had been implemented in Art and Design Department of Faculty of Letters in Universitas Negeri Malang learning and teaching practices, it is proven that online based works better as supplement rather than main learning platform.Studio time in printmaking course becomes less relevant in this crisis, thus making prints at home with available and homely tools can make students relate printmaking principles to real life situation.The methodology of this research is experimentation, observation finished by qualitative study of the students' progress.Although challenges may appear enlarged, the result until now is quite comprehensive that students are exploring more tools and material in order to understand basic printmaking principles.On side note, because the students are using easy obtainable materials, the process also reduces more chemical residue and appear more friendly for future greener art curriculum.
Evaluare și intervenție logopedică în contextul întârzierii în dezvoltarea limbajului

According to the literature, we are talking about a language delay when the child fails to reach the level of language evolution, compared to his chronological age. In fact, we know that language development is closely linked to cognitive development. Therefore, the structure of language is influenced by the degree of cognitive development. Knowing that the language delay may exists as a secondary pathology in most intellectual disabilities and can be recognized by the poverty of vocabulary, the inability to express themselves correctly in sentences or phrases, being reached phonetic, lexical and grammatical aspects, I wanted to surprise in this study the features of language development in young children oriented towards our school by proposing a complex, multidimensional approach to early assessment of language skills and capturing the effect of a multidimensional intervention program that will ultimately lead to the configuration of functional communication skills. Children included in this study are enrolled in the kindergarten group or in the preparatory class, aged between 4 and 8 years. These children are also diagnosed with mild or moderate intellectual disability. The program took place in two stages. In the first stage, a multidimensional evaluation was performed, on all sides of the language by applying tests, scales or evaluation tests. The results obtained by the children were interpreted and presented at the level of some case studies. In the second stage, we proposed an intervention program that aims to enrich and activate vocabulary, by applying the "50 word-list techniqueT⁵⁰", training phonetic and phonological skills, those related to structural semantics and lexical semantics and shaping morphological skills, which will lead to the configuration of a functional communication skill related to chronological and mental age.
Multi-agent reinforcement learning for convex optimization

Multi-Agent Reinforcement Learning (RL) algorithms have recently demonstrated their ability to discover/develop new strategies to solving problems. This work aims to apply this same methodology to solving convex optimization problems in an attempt to train agents to outperform popular optimizers. This work will examine methods to frame the problem to the RL agents which encourage rapid convergence. Additionally we will discuss the various ways to represent the environment or state that the agents will operate in, as well as the action space and rewards.
Comparisons of Energy Efficiency Management Techniques in Cloud Based Environment

Cloud computing has been transforming large part of various IT services into virtualization which are made available as a utility services and scheduling plays a vital role here to get maximum utilization from various physical resources. Cloud Computing has been offering its virtual resources and services anywhere and anytime on pay-as-you-go basis. So, Cloud Computing becomes a backbone in the various fields of Industry and Academia for providing the storage backups, load balancing and most importantly providing the dynamic resource scheduling on a real-time basis. Therefore, energy consumption will be very high by the various clod data centers which lead to very high amount of operational cost and harm the environment as well. Hence, there is a requirement of Green Computing for the sustainability and ensuring the energy efficiency by energy efficient techniques. In present scenario, various energy efficient algorithms have been proposed by the researchers which reduce the energy efficiency of cloud environment. But all these algorithms have been evaluated using the same experimental environment which gives the related results and it becomes difficult for the researchers to choose the best algorithm among them. So to provide the better understanding on the various algorithms and helps researchers for choosing the best suitable algorithm, we have compared the various state of art energy efficient algorithm in more detailed way considering various prospective. Along with the comparison, we have also done studied the overview of investigated algorithm. Finally, we have the literature survey of various others related to different parameters and techniques. From the same literature survey, we have come to the conclusion of each author’s statement.
Sentiment-based Candidate Selection for NMT

The explosion of user-generated content (UGC)--e.g. social media posts, comments, and reviews--has motivated the development of NLP applications tailored to these types of informal texts. Prevalent among these applications have been sentiment analysis and machine translation (MT). Grounded in the observation that UGC features highly idiomatic, sentiment-charged language, we propose a decoder-side approach that incorporates automatic sentiment scoring into the MT candidate selection process. We train separate English and Spanish sentiment classifiers, then, using n-best candidates generated by a baseline MT model with beam search, select the candidate that minimizes the absolute difference between the sentiment score of the source sentence and that of the translation, and perform a human evaluation to assess the produced translations. Unlike previous work, we select this minimally divergent translation by considering the sentiment scores of the source sentence and translation on a continuous interval, rather than using e.g. binary classification, allowing for more fine-grained selection of translation candidates. The results of human evaluations show that, in comparison to the open-source MT baseline model on top of which our sentiment-based pipeline is built, our pipeline produces more accurate translations of colloquial, sentiment-heavy source texts.
IMAGE PROCESSING TECHNIQUE USED IN ROAD TRAFFIC ANALYSIS – OPPORTUNITIES AND CHALLENGES

Video detection of vehicles in road traffic can measure traffic parameters like traffic flow, speed, and density. This article presents the opportunities and challenges encountered in applying the image processing technique to video files obtained through the video camera of a drone. The purpose of this research is to highlight the opportunities and challenges encountered in video detection using a vehicle counting application developed in Matlab. The results show that the main opportunities are related to providing an overview of the traffic on an analysed road segment, but also to obtaining the most important traffic parameter, vehicle flow. The challenges are closely related to the weather conditions that directly influence video detection.
Semantic feature representation and interpretation with context-free grammar and push-down automaton

Two key research issues are addressed: (i) A semantic representation and interpretation framework by using a lightweight self-supervised learning approach, namely the Context-Free Grammar and Push-Down Automaton; and (ii) A mobile phone App implementation of B-mode medical ultrasound imaging with a handheld probe, which can make use of the learned semantic features of scanned images for future home-based health screening.
Machine Learning Frameworks

No AccessNeuronale Netze mit C# programmierenApr 2021Machine Learning FrameworksDaniel BaslerDaniel BaslerSearch for more papers by this authorhttps://doi.org/10.3139/9783446464261.007SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2020Pages: 173-219Print ISBN: 978-3-446-46229-8eISBN: 978-3-446-46426-1 Copyright & Permissions© 2021 Carl Hanser Verlag GmbH & Co. KGPDF downloadLoading ...
LibGuides: SCHS Library Book Toolbox: Nonfiction

This helpful LibGuide will give you some tools and tricks to find a young adult book that genuinely interests you. Are you more interested in fantasy? Nonfiction? Have no fear, this LibGuide will have something for you. Resources for finding nonfiction books!
VGNMN: Video-grounded Neural Module Network to Video-Grounded Language Tasks

Neural module networks (NMN) have achieved success in image-grounded tasks such as Visual Question Answering (VQA) on synthetic images. However, very limited work on NMN has been studied in the video-grounded dialogue tasks. These tasks extend the complexity of traditional visual tasks with the additional visual temporal variance and language cross-turn dependencies. Motivated by recent NMN approaches on image-grounded tasks, we introduce Video-grounded Neural Module Network (VGNMN) to model the information retrieval process in video-grounded language tasks as a pipeline of neural modules. VGNMN first decomposes all language components in dialogues to explicitly resolve any entity references and detect corresponding action-based inputs from the question. The detected entities and actions are used as parameters to instantiate neural module networks and extract visual cues from the video. Our experiments show that VGNMN can achieve promising performance on a challenging video-grounded dialogue benchmark as well as a video QA benchmark.
Virtual environments for the design of electronic devices as a means of instrumental digital didactics

Instrumental digital didactics reflects the application to education of various digital means of obtaining, processing and interpreting experimental data in accordance with the logic of the scientific method and engineering design. An important component of a modern STEM-oriented educational environment are innovative software products for modelling and simulation of electronic circuits. In educational research projects on their basis, the parameters of the components of electrical circuits created in virtual environments are compared with the technical characteristics of similar devices available for sale. This technique allows not only to demonstrate the similarities and differences of idealized and real artifacts, to identify sources and magnitude of possible errors, but also to obtain electrical characteristics sufficient to build equivalent schemes for substituting devices without prior experimental research. The proposed approach is demonstrated on the example of studying the parameters of a device of current interest — a photoelectric converter (determining the point of its maximum power and the fill factor). Learning by means of using equivalent substitution schemes demonstrates one of the variants of the engineering design process to students. In addition, the proposed method, due to the possibility of development from an algorithmic training procedure to an engineering research, allows for an individual approach to the teaching of electrical engineering and electronics. Research of didactic features of the study of electricity and the basics of electronics, in particular with the use of NI “Multisim 11.0”, is one of the activities of the STEM-laboratory of “MANLab” National centre “Junior academy of sciences of Ukraine”. Some of the tested methods are designed as a workbook and placed in free access in the form of instructions on the online resource stemua.science.
Infrastructure as Code with HCL

Infrastructure as Code has become more relevant, which in turn has led to the increased adoption of HashiCorp products. In this chapter, you will learn how to use the HashiCorp product suite to design and implement full Infrastructure as Code.
PENGEMBANGAN APLIKASI BANK SAMPAH BERBASIS WEBSITE (STUDI KASUS: KSM BIMA KELURAHAN TELUK PURWOKERTO SELATAN)

The Garbage Bank KSM BIMA is one of the Garbage Banks located in the Kelurahan of Teluk, Subdistrict of South Purwokerto, which operates independently in the field of waste management. The current management of the waste bank is still done manually, so that officers experience difficulties including when a member who is saving money is constrained by the price of waste, which sometimes changes the price of waste, which makes officers have to carefully look at the current garbage price table. The next obstacle is that by reporting the recorded savings results, there are sometimes differences in balances with the waste bank manager and difficulties in making a report on the amount of waste per category and a report on the state of cash. This study aims to develop a garbage bank application in order to facilitate officers in managing the garbage bank data, as well as improve the quality of service to customers and partners of waste. This application was developed with the PHP programming language and Codeigniter Framework, while the system development method used in making applications is to use the waterfall method. Validation of functionality testing using the blackbox testing method and 100% valid test results from 30 test cases provide results in accordance with the test plan and find no errors in running the application functions.