Reviewerly/exHarmony huggingface dataset:
LRAP

In contemporary context aware systems when an event is triggered, system responds through sense-decide-actuate cycle and carries out requisite task with corresponding processing expenditure. However, such systems are lacking the capability to establish learned association among subsequent events, furthermore, the notion of the context is embedded into applications that may pose certain processing overhead. Therefore, each event is considered as arrival of afresh situation and dealt with entirely replicated processing cycle. Such computing mechanism where each event is stimulus to complete resource utilization leads the system towards processing overwhelming. This research work proposes a LRAP: a Learned Reflex action embedded Associative context learning based processing efficient Paradigm in visual sensor networks. In which each actuation of the system serves as new context to succeeding event and aids it to evolve internally. Gradually, with exposition to multiple events system refines its context repository with introspective context extracted through processed retrospective context that serves as meta-context to upcoming events leading the system towards evolution of context addition. Such context learning through improved introspective context utilization maximizes the system internal actuation that further optimizes the independent functions of reduced sensing and improved decision with minimal resource exploitation evolving it as a cut-through processing mechanism. Furthermore, when system gains the maximum internal actuation it responds impulsively against repetition of an event through intro-spectively evolved actuation based associative learning that imitates learned reflex action through associative context learning leading the system towards exceedingly processing efficient paradigm.
Exploring Teachers' Value Beliefs of Integrating Digital Literacy in K-12 Classrooms

The purpose of the exploratory study was to investigate teacher value beliefs of integrating digital literacy into their classrooms. Participants were forty-six teachers enrolled in an educational technology course. Data was collected from online survey and asynchronous online discussions. Findings suggest that teachers' value beliefs for integrating digital literacy into their classrooms are influenced by increasing student motivation and engagement in the classroom, modernizing the curriculum, making it more relevant to the students, and building student ability to be thoughtful readers and interpreters of the media messages. These results recommend that professional development activities should emphasize the potential impact of digital literacy on student learning and to take advantage of specific digital tools that support teacher value beliefs.
Rainfall forecasting using parallel and distributed analytics approaches on big data clouds

In cloud environment, big data analytics is a very innovative idea;Big data is defined as a very huge quantity of data. To extract meaning full value from it we need different technologies for analysis of the data. Big data Analytic is used to study pattern of historical data and the effect of changes of different parameters. Rainfall Forecasting has been a standout amongst the most intriguing and captivating space and it assumes a noteworthy part in meteorology and daily human life. Rainfall forecasting is used to estimate the probability of whether it will rain or not. Rainfall situation is the state of atmosphere at a given time in terms of different parameter like temperature, humidity and wind speed. Forecasting of rainfall is important to facilitate preparing for the finest and the nastiest of the climate. India is agrarian country so it is essential to know in advance about climate. This paper presents big data analytics for Rainfall forecasting and studies the advantage of using it. The algorithm which is used in this project is Time Series. The intention of time series analysis is generally in two ways, one is to recognize or representation the stochastic mechanisms that gives rise to an observed series and second is to predict or forecast the future value of an arrangement in light of the historical backdrop of that arrangement.Forecasts are prepared for new data when the actual result may not be known until some future date. What’s to come is being anticipated, yet all earlier perceptions are quite often treated similarly.
L‐CNN: a lattice cross‐fusion strategy for multistream convolutional neural networks

This paper proposes a fusion strategy for multistream convolutional networks, the Lattice Cross Fusion. This approach crosses signals from convolution layers performing mathematical operation-based fusions right before pooling layers. Results on a purposely worsened CIFAR-10, a popular image classification data set, with a modified AlexNet-LCNN version show that this novel method outperforms by 46% the baseline single stream network, with faster convergence, stability, and robustness.
Evaluation of user interface design metrics by generating realistic‐looking dashboard samples

Abstract The analysis of user interfaces using quantitative metrics is a straightforward way to quickly measure interface usability and other various design aspects (such as the suitability of page layout or selected colors). Development and evaluation of objective metrics corresponding with user perception, however, usually requires a sufficiently large training set of user interface samples. Finding real user interface samples might not be easy. Therefore, we rather use generated samples. In such case, we need to provide a realistic‐looking appearance of samples. This paper describes a workflow of the preparation of such samples. It presents a configurable generator based on the composition of simple widgets according to a predefined model. It also describes a reusable library for simple creation of widgets using capabilities of the JavaScript framework Vue.js. Finally, we demonstrate the applicability of the generator on a generation of dashboard samples which are used to evaluate existing metrics of interface aesthetics and show the possibility of their improvement.
Development of Augmented Reality Applications for the Entire Spectrum of Screw Compressor Manufacturing Process

Abstract Augmented Reality (AR) in industrial applications connects users across digital and physical worlds and coupled with rapid return of investment, easy adoption, and as a meaningful way to stand out from the competition, AR is quickly becoming increasingly common in the industrial world. This paper first provides the reader with a description on what exactly is augmented reality and the difference between augmented reality and virtual reality, before moving onto describing the scope of this new application for an organisation like Howden. A brief analysis of how augmented reality is quickly finding a footing within this sphere is offered alongside an overview of the main players and actors. The paper then focuses on how the 3D CAD modelling already completed by the research and development, bare-shaft engineering and package engineering teams can be utilised in the rapid development of augmented reality applications. Examples of how Howden are developing and using these AR applications are then given with specific reference to the Screw sales teams using them to show prospective clients the model of their package / machine; the Screw package build teams sharing knowledge using AR during package fit-out and hazard and operability studies; the bare-shaft test team using AR within their acoustic enclosure and finally the impact of AR in the service teams. The paper concludes with how, as this technology becomes more mainstream, manufacturers adopting AR can significantly add value to their customers experience both in terms of service and satisfaction but also differentiate themselves from their competitors.
Social Media for Smart Farmer-Shared Farming Equipment Model

This research offers a roadmap for creating a concept for building a collaborative and connected mobility model to share the agricultural machine. The study aims to adopt those models to create a collaborative and connected mobility model as a Shared Agricultural Machine Network for Smart Farmer. The fact that Thai farmers are facing the aging crisis, like all other industries and farming machinery has become a need for manpower replacement to keep their earning for livings. Unfortunately, these machines are often expensive, so they can own only a few, and when it comes to reaping the harvest season, and they often require different tools for specific purposes. Our survey has shown that farmers have different ways of cultivating different crops at the same time this implies that in harvesting season there are unused agricultural pieces of equipment available to share among them. The model of shared farm equipment could lead to new farmersâ€™ way of life and itâ€™s time to become smart-farmers. This paper will discuss important considerations, including the need for challenges, trends, and opportunities for farmers to have machinery when needed and to share what idle with others via a peer-to-peer network using mobile application platform.
Neural Network Processing Model for Data Flow Optimization

This paper focuses on a new concept of processing data flow in networks, a process which will take place over the network, using NPU powered routers managing data and evaluating packet flow and post process. Imagine all this data which wait in the queues of routers, processed in a few nodes before delivery.
Competence and Confidence through Technology Enhanced Language Learning-The Impact of Technology Among Rural and Semi-Urban Undergraduates of Engineering in India: A Study

The present study is an assessment of the efficacy of technology enhanced language learning (TELL) in teaching English to undergraduates pursuing third year of Engineering program using specific modules based on 'Internet and Smart Phone' in the Advanced English Communication Skills Lab (AECS). After a pilot study, students were divided into two groups and a group of them were deliberately asked to use internet both in their computers and smart phones whereas others were exposed to designed software in the lab. They were given authentic tasks such as watching selected documentaries, presentations on selected CEOs of pioneering technical firms along with selected successful entrepreneurs of India. Consequently, students were asked to read and write case studies and assessed through presentations, group discussions and mock interviews. Remarkably, the outcomes revealed that TELL facilitated the target students of rural and semi urban areas significantly in enhancing their language competence, and, therefore, confidence more effectively than the other group of students who used designed software in the lab.
Testing Real-Time Applications on Windows 10 IOT Using the Nyquist Theory

Abstract Raspberry Pi is a mini-computer that is provided to carry out activities quickly and precisely, but Raspberry Pi was created to not be able to do the real-time system with the support of Windows 10 IoT operating system, so the real-time system can be done on Raspberry Pi. The real-time applied in the application needs to be tested with the Nyquist theory. The purpose of this study was to get real-time system measurements available on Windows 10 IoT. This test is done using the Nyquist theory by calculating the results of measurements on mp3 streaming performed on Windows 10 IoT.
Post-training Quantization of Deep Neural Network Weights

The paper considers the quantization of weights as a tool for reducing the original size of an already trained neural net without having to perform the retraining. We have examined the methods based on uniform and exponential weight quantization and compared the results. Besides, we demonstrate the use of the quantization algorithm in three neural nets: VGG16, VGG19 and ResNet50.
Evaluation of Continuous Image Features Learned by ODE Nets

Deep-learning approaches in data-driven modeling relies on learning a finite number of transformations (and representations) of the data that are structured in a hierarchy and are often instantiated as deep neural networks (and their internal activations). State-of-the-art models for visual data usually implement deep residual learning: the network learns to predict a finite number of discrete updates that are applied to the internal network state to enrich it. Pushing the residual learning idea to the limit, ODE Net—a novel network formulation involving continuously evolving internal representations that gained the best paper award at NeurIPS 2018—has been recently proposed. Differently from traditional neural networks, in this model the dynamics of the internal states are defined by an ordinary differential equation with learnable parameters that defines a continuous transformation of the input representation. These representations can be computed using standard ODE solvers, and their dynamics can be steered to learn the input-output mapping by adjusting the ODE parameters via standard gradient-based optimization. In this work, we investigate the image representation learned in the continuous hidden states of ODE Nets. In particular, we train image classifiers including ODE-defined continuous layers and perform preliminary experiments to assess the quality, in terms of transferability and generality, of the learned image representations and compare them to standard representation extracted from residual networks. Experiments on CIFAR-10 and Tiny-ImageNet-200 datasets show that representations extracted from ODE Nets are more transferable and suggest an improved robustness to overfit.
GameRecs: Video Games Group Recommendations

Video games are a relatively new form of entertainment that has been rapidly gaining popularity in recent years. The number of video games available to users is huge and constantly growing, and thus it can be a daunting task to search for new ones to play. Given that some games are designed to be played together as a group, finding games suitable for the whole group can be even more challenging. To counter this problem, we propose a content-based video game recommender system, GameRecs, which works on open data gathered from Steam, a popular digital distribution platform. GameRecs is capable of producing both user profiles based on Steam’s user data, as well as video game recommendations for those profiles. It generates group recommendations by exploiting lists aggregation methods, and focus on providing suggestions that exhibit some diversity by using a k-means clustering-based approach. We have evaluated the usability of GameRecs in terms of the user profile generation and the produced video game recommendations, both for single users and for groups. For group recommendations we compared two recommendation aggregation methods, Borda Count and Least Misery method. For diversity evaluation we compared results with and without the proposed k-means clustering method.
Language Modeling at Scale

We show how Zipf's Law can be used to scale up language modeling (LM) to take advantage of more training data and more GPUs. LM plays a key role in many important natural language applications such as speech recognition and machine translation. Scaling up LM is important since it is widely accepted by the community that there is no data like more data. Eventually, we would like to train on terabytes (TBs) of text (trillions of words). Modern training methods are far from this goal, because of various bottlenecks, especially memory (within GPUs) and communication (across GPUs). This paper shows how Zipf's Law can address these bottlenecks by grouping parameters for common words and character sequences, because U ≪ N, where U is the number of unique words (types) and N is the size of the training set (tokens). For a local batch size K with G GPUs and a D-dimension embedding matrix, we reduce the original per-GPU memory and communication asymptotic complexity from Θ(GKD) to Θ(GK + UD). Empirically, we find U ∝ (GK)^0.64 on four publicly available large datasets. When we scale up the number of GPUs to 64, a factor of 8, training time speeds up by factors up to 6.7× (for character LMs) and 6.3× (for word LMs) with negligible loss of accuracy. Our weak scaling on 192 GPUs on the Tieba dataset shows a 35% improvement in LM prediction accuracy by training on 93 GB of data (2.5× larger than publicly available SOTA dataset), but taking only 1.25× increase in training time, compared to 3 GB of the same dataset running on 6 GPUs.
A COMPARISON OF DEEP LEARNING BASED ARCHITECTURE WITH A CONVENTIONAL APPROACH FOR FACE RECOGNITION PROBLEM

This paper addresses a new approach for face recognition problem based on deep learning strategy. In order to verify the performance of the proposed approach, it is compared with a conventional face recognition method by using various comprehensive datasets. The conventional approach employs Histogram of Gradient (HOG) algorithm to extract features and utilizes a multi-class Support Vector Machine (SVM) classifier to train and learn the classification. On the other hand, the proposed deep learning based approaches employ a Convolutional Neural Network (CNN) based architecture and also offer both a SVM and Softmax classifiers respectively for the classification phase. Results reveal that the proposed deep learning architecture using Softmax classifier outperform conventional method by a substantial margin. As well as, the deep learning architecture using Softmax classifier also outperform SVM in almost all cases.
Analysis of Artificial Neural Networks Method Backpropagation to Improve the Understanding Student in Algorithm and Programming

Abstract To Improve the student skills in learning is constitute hope of every lecturer. For/In computer base-study programme master of programming is needed. the Algorithms and Programming are basic course that must be studied. Mean while/that for, using the backpropogation methods which is part of the Artificial Neural Networks will test and train data by using the backpropogation algorithm to improve students ability to understand the subject. The result obteined can be analyzed about the results if the output is that the closed to the target then the trainning process can be finished. Furthermore/Hereafter, The average error can be calcutate obteined from the result of the trainning that has been carried out. If the results have not reached then the backpropagation method always do iteration with make to update weignt until maximal output and can to get output of exelent with used backpropagation method.
Z-Dedup:A Case for Deduplicating Compressed Contents in Cloud

Lossless data reduction techniques, particularly compression and deduplication, have emerged as effective approaches to tackling the combined challenge of explosive growth in data volumes but lagging growth in network bandwidth, to improve space and bandwidth efficiency in the cloud storage environment. However, our observations reveal that traditional deduplication solutions are rendered essentially useless in detecting and removing redundant data from the compressed packages in the cloud, which are poised to greatly increase in their presence and popularity. This is because even uncompressed, compressed and differently compressed packages of the exact same contents tend to have completely different byte stream patterns, whose redundancy cannot be identified by comparing their fingerprints. This, combined with different compressed packets mixed with different data but containing significant duplicate data, will further exacerbate the problem in the cloud storage environment. To address this fundamental problem, we propose Z-Dedup, a novel deduplication system that is able to detect and remove redundant data in compressed packages, by exploiting some key invariant information embedded in the metadata of compressed packages such as file-based checksum and original file length information. Our evaluations show that Z-Dedup can significantly improve both space and bandwidth efficiency over traditional approaches by eliminating 1.61% to 98.75% redundant data of a compressed package based on our collected datasets, and even more storage space and bandwidth are expected to be saved after the storage servers have accumulated more compressed contents.
Scientists’ Contribution to Science and Methods of Its Visualization

In the paper, fuzzy sets technology is used to find out the levels of the scientific contribution of a scientist or a team of scientists into scientific fields or discipline. Namely, fuzzy logic is applied to describe the fuzzy input and output relations between scientific fields, disciplines, journals and researchers, afterward, the compositions of these relations and the proposed optimistic fuzzy aggregation norm over fuzzy variables are used. Two techniques are applied to show the contribution to the science of the team of researchers graphically; one graphical method uses the radar graphs, and the other one is the application of the t-SNE technique based on the Euclidean metrics in the space of the scientists’ contribution into the different scientific fields. Moreover, the proposed methods are the foundation for the web-software “ScienceFuzz” developed for calculations levels of contribution to the sciences by an individual researcher or by a team of them.
An Entropy-Based Inertia Weight Krill Herd Algorithm

The krill herd (KH) algorithm is an emerging meta-heuristic algorithm for solving complex problems. Though it is robust in optimization, there are some parameters that need to be fine-tuned for improved performance. This paper proposes an entropy-based inertia weight krill herd (EBIWKH) algorithm, which could adaptively adjust the inertia weight according to the variance of population entropy. It is tested on CEC2017 benchmark functions and compared with other inertia weight adjustment strategies. Experimental results show that the EBIWKH algorithm is more robust and stable than other compared algorithms.
A Generic Formal Specification of an Infinite Runner Games for Handheld Devices Using Z-Notation

The exponential increase in usage of games in daily life has been seen in the present era. The use of mobile games is rapidly increasing. Mobile games are now considered a useful tool for learning and educational practices. This research mainly focuses on development and game mechanics of endless runner games for hand-held devices. This research will facilitate the developers in understanding the game mechanics of endless runner games. A generic algorithm for endless/ infinite runner games is proposed. The formal specification of the proposed algorithm for developers is presented using Z-Notation. The study presents the evidence of translation of algorithm in Z-Notation. The FUZZ Type checker is used to validate Z-Schema of the Algorithm. The presentation of game mechanics through formal specification will help developers during development and testing phase. The proposed Z-Notation schema can also be extended for other games.
Improved dark channel prior image dehazing algorithm based on wavelength compensation

For the dark channel prior image dehazing algorithm to defog the foggy image with the same transmittance, the recovery result will have a more serious color shift problem. An image defogging algorithm based on different color wavelength compensation is proposed. Firstly, the median dark channel map is obtained by the median filtering method, and then the optical attenuation coefficients of different wavelengths are calculated to obtain the transmittance of Red-Green-Blue (RGB) three channels. Finally, the revised parameters are substituted into the atmospheric scattering model to restore the fog-free image. The experimental results show that the foggy image containing bright areas such as the sky have a good processing effect, which significantly reduces the color distortion of the bright area, and the image is clearer and more natural.