Reviewerly/exHarmony huggingface dataset:
PydNet: An Efficient CNN Architecture with Pyramid Depthwise Convolution Kernels

Convolutional neural networks (CNNs) have shown significant performance in solving various artificial intelligence tasks in recent years. However, the increasing model size has raised challenges in adopting them in limited-resource applications. Recently, many research works try to build efficient networks which are as small as possible and have small computation time while still have acceptable performance. A state-of-the-art architecture is ShuffleNetV2. It has Depthwise Separable Convolution (DWConvolution) in place of standard Convolution to reduce the model size. Its design is very efficient which follows many practical guild-lines. The main weakness of ShuffleNetV2 is small receptive field. This paper proposes an improved version ShuffleNetV2 which instead of using just a 3×3 kernel size for DWConvolution, it uses a pyramid kernel sizes to capture spatial information in many scales. The proposed architecture is evaluated on a highly competitive object recognition benchmark datasets (CIFAR-100). The experiments demonstrate that the proposed network achieves better performance compared with ShuffleNetV2 as well as other state-of-the-art networks in term of similar model size.
A Gradient-Based Spatial Color Algorithm for Image Contrast Enhancement

This work presents GREAT-Mix, a novel contrast enhancer derived from the combination of the Retinex-inspired spatial color algorithms STRESS and GREAT. These algorithms improves the visibility of the details and content of any input image by adjusting its colors on the basis of local spatial and visual features processed channel by channel. Precisely, STRESS stretches the intensity I(x) of each pixel x between two bounding values corresponding to the minimum and maximum intensities of sets of pixels randomly sampled with radial density around x, while GREAT re-scales I(x) by a factor obtained by processing a set of edges deterministically selected over the image. GREAT-Mix implements the stretching function of STRESS but determines its bounding values from the edges sampled by GREAT. The result is a new spatial color algorithm that performs contrast enhancement similarly to STRESS, but that, thanks to the deterministic sampling of GREAT, grants robustness to noise and repeatability of the outcomes.
Support Vector based Indoor Scene Classification Technique using Different Features

In today's World machines have started ruling the human beings as the machines are performing almost every task what human beings are capable of doing. Scene classification is one such term which is gaining importance where machines imitate the behaviour of a human being. Scene classification could be performed either on indoor scenes or on outdoor scenes using various techniques of feature extraction and out of these two categories indoor scene classification is considered more challenging. The indoor scene classification techniques have the problem of poor accuracy. In this work, indoor scene classification is addressed for improving the accuracy using the integration of SIFT (Scale Invariant Feature Transform), SURF (Speeded-Up Robust Feature) & Tamura features for the purpose of feature extraction and then using SVM (Support Vector Machine) for feature matching. The results of the algorithm are evaluated using MIT-Indoor dataset and the experimental results show that the proposed technique outperforms various existing techniques for indoor scene classification.
Research on unbalanced training samples based on SMOTE algorithm

Abstract In the classification tasks of deep learning, unbalanced data distribution of training samples is a serious problem. In this case, the deep neural networks will bias to the majority-class samples and can not learn data characteristics of the minority-class samples well, making it difficult to identify the minority-class samples. To improve the performance of deep neural networks on minority-class samples, this paper proposes a novel scheme based on SMOTE algorithm, which generates new minority-class samples to balance the training dataset. Compared with the traditional over-sampling operations commonly used in image classification tasks such as copying images simply, image flipping, color adjustment etc, our scheme generates new minority-class samples, which improves the features of minority-class samples and avoids the over-fitting problem in classification. The workflow of image preprocessing and SMOTE over-sampling operation are introduced in this paper. The selection basis of the network model and the comprehensive model indicators are also presented. Comparative experiments are performed by testing models trained respectively on unbalanced dataset and balanced dataset after SMOTE over-sampling operation. Results shows that the proposed scheme is feasible and effective to improve the neural network’s ability of identifying minority-class samples in classification tasks.
Hyperparameters Optimization in Scalable Random Forest for Big Data Analytics

With the tremendous growth of big data, the issue of how to get valuable knowledge from these data becomes the main attention for researchers. The traditional analytic platforms and methods are not fit for processing such big data. Big data analytics plays a vital role in the research area for processing the unrelated, structured, and unstructured data. Machine learning algorithms are needed to improve for exploiting the opportunities hidden inside in big data. In this paper, the performance of Scalable Random Forest algorithm (SRF) is improved by hyperparameters optimization and dimension reduction technique. Big data analytics platform is developed using the Hadoop Distributed File System and Spark processing engine. The performance evaluation of improved SRF is conducted over real-world data center workload traces and model validation process is carried out to obtain the reliable error estimation for big data analytics. Our findings show that the tuning of hyperparameters is critical for different datasets, and optimization of these parameters significantly enhances prediction accuracy than the default parameters.
Research on University Evaluation of Teaching Quality Based on Data Driven

Traditional method weakens the importance of some evaluation indexes, which leads to the unreliability of the evaluation results. Therefore, a new method for evaluating the education quality of College Students based on data driven is proposed. The paper constructed a data driven model with the system state variables as input and output, and established the mapping relation between the system state variables through the data characteristics of the system. The evaluation index system is consisted of three levels, and the weights of each index are determined by the subjective and objective rights reorganization in the sense of least squares. The evaluation of College Students' education quality is realized by using multi-level fuzzy logic Association clustering evaluation method. The experimental results show that the proposed method which used to evaluate the training quality of college students is scientific and reliable, and the results are consistent with the actual results.
A Modelling Language to Support the Evolution of Multi-tenant Cloud Data Architectures

Multi-tenant data architectures enable efficient resource utilization in cloud applications, but are currently being implemented in industry and research using manual coding techniques that tend to be time consuming and error prone. We propose a novel domain-specific modeling language, CadaML, to automatically manage the development and evolution of cloud data architectures that (a) adopt multi-tenancy and/or (b) comprise of a combination of different storage solutions such as relational and non-relational databases, and blob storage. CadaML provides concepts and notations to support abstract modelling of a multi-tenant data architecture, and also provides tools to validate the data architecture and automatically produce application code. We rigorously evaluate CadaML through a user experiment where developers of various capabilities are asked to re-architect the data layer of an industrial business process analysis application. We observe that CadaML users required 3.5x less development time than manual coders. In addition to improved productivity, CadaML users highlighted other benefits gained in terms of reliability of generated code and usability.
Non-marine ostracod biostratigraphy of Cretaceous rift lake deposits (Sousa Basin, Brazil): Paleogeographical implications and correlation with Gondwanic basins

The Cretaceous section of the Sousa Basin represents alluvio-fluvio-lacustrine deposits from rift lakes generated in NE Brazil as a consequence of the West Gondwana break-up process, marked by progressive separation of the African and South American plates. The chronostratigraphic age of the sedimentary succession is controversial due to the few biostratigraphic data available, based on palynomorphs, ostracods and ‘conchostracans’ (Spinicaudata), and taxonomic problems of some of the fossils. Non-marine ostracods (microcrustaceans) are useful index fossils and have been used for a regional biozonation of the lacustrine deposits. Due to this factor non-marine ostracods from the core 2-FC-01-PB, which comprises rocks of the Sousa Formation (Rio do Peixe Group), were analysed. The core 2-FC-01-PB assemblage includes index taxa for biozones RT-002 to RT-005 (Alicenula leguminella, Brasacypris ovum, Cypridea ambigua and Reconcavona swaini), indicating a Berriasian to Hauterivian age (Rio da Serra/Aratu local stages). The previous assignment of a Jurassic age to the early stage of deposition of the Rio do Peixe Group is refuted here. We discuss and revise the stratigraphic range and paleogeographical distribution of relevant index ostracods. Our revised non-marine ostracod biostratigraphy facilitates correlations of non-marine strata of the Sousa Basin with those of Brazilian (Recôncavo, Tucano and Sergipe) and African (Gabon and Congo) sedimentary basins of West Gondwana.
Is Blockchain the Next Step in the Evolution Chain of [Market] Intermediaries?

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Analyzing User Experiences in Incorporated Fictionality

In this paper, we present an overview of a case study that aims to augment the meaning of real spaces through incorporated fictionality. The case study uses a head mounted display to show real spaces by capturing it through a camera, but the space is augmented from the true real spaces. Our focus, in particular, is criticizing how one can feel a sense of reality in augmented real spaces in a case study because the meaning of the augmented real space is lost if a user does not feel a sense of reality on the incorporated fictionality. We extract some insights and potential pitfalls from the analyses of the case study within participatory design workshops.
Analysis of Artificial Neural Network in Predicting the Fuel Consumption by Type of Power Plant

Abstract The electric is one of the needs for human, the growth of the electric power in Indonesia is very increased. There are 13 types of power plants in Indonesia, including to require the fuel in its operational. Fuel consumption needs to be recorded at regular intervals so that the needs of fuel for the power plant remain fulfilled. This research discusses about the predictions fuel consumption based on the type of power plants. The method used is the Artificial Neural Network with the back propagation algorithm. This method is good enough to use in predicting the data. The data used is fuel consumption data based on the type of power plant of the year 2014-2016. The best architecture is 8-23-1 that gets results accuracy of 88%, epoch 6016 iteration and MSE 0.0005509801. From these results then predict using the 8-23-1 architecture is the best architecture.
Analysis of Backpropagation Algorithms in Predicting World Internet Users

Abstract The internet is now a primary need for its users. According to the e-Marketer market research institute, there are the top 25 countries with the most internet users in the world. Indonesia is in the sixth position with a total of 112.6 million internet users. With the increasing number of internet users, it is expected to be able to contribute to advancing the economy and education in a country. To be able to increase the number of internet users, especially in Indonesia, it is necessary to predict in the coming years so that the government can provide adequate facilities and infrastructure in order to compensate for the growing number of internet users and as a precaution when there is a decrease in the number of internet users. The data used in this study focus on the data on the number of internet users in 25 countries in 2013-2017 sourced from the Indonesian Ministry of Communication and Information. The algorithm used is the Backpropagation Neural Network. Data analysis was performed using Artificial Neural Network method using Matlab R2011b. This study uses 5 architectural models. The best network architecture produced is 3-50-1 with an accuracy rate of 92% and the Mean Squared Error (MSE) value is 0.00151674.
Personalized Resource Recommendation Based on Collaborative Filtering Algorithm

Abstract Digital libraries can satisfy people’s demand for books and real-time information resources, but because digital libraries have huge resources, users cannot find the resources they need in time, at this time, personalized recommendation methods become an effective way to solve the problem. This paper takes collaborative filtering algorithm based on user recommendation as the research object, analyses the principle of the algorithm and the problem of sparse scoring data, an improved collaborative filtering algorithm is proposed, the algorithm sets scoring by borrowing time, and takes the sum of multiple user scoring as the scoring among users, then, according to the similarity of book content, the user’s scoring of non-scored books is predicted. The proposed algorithm is applied to the digital book recommendation system to obtain better recommendation performance, compared with traditional methods, and the recommendation system based on this algorithm can get more recommendation results close to the needs of readers.
Efficient Multi-Point Local Decoding of Reed-Muller Codes via Interleaved Codex

Reed-Muller codes are among the most important classes of locally correctable codes. Currently local decoding of Reed-Muller codes is based on decoding on lines or quadratic curves to recover one single coordinate. To recover multiple coordinates simultaneously, the naive way is to repeat the local decoding for recovery of a single coordinate. This decoding algorithm might be more expensive, i.e., require higher query complexity. In this paper, we focus on Reed-Muller codes with usual parameter regime, namely, the total degree of evaluation polynomials is d = Θ(q), where q is the code alphabet size (in fact, d can be as big as q/4 in our setting). By introducing a novel variation of codex, i.e., interleaved codex (the concept of codex has been used for arithmetic secret sharing), we are able to locally recover arbitrarily large number k of coordinates of a Reed-Muller code simultaneously with error probability exp(-Ω(k)) at the cost of querying merely O(q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> k) coordinates. It turns out that our local decoding of Reed-Muller codes shows (perhaps surprisingly) that accessing k locations is in fact cheaper than repeating the procedure for accessing a single location for k times. Precisely speaking, to get the same success probability by repeating the local decoding algorithm of a single coordinate, one has to query Ω(qk <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) coordinates. Thus, the query complexity of our local decoding is smaller for k = Ω(q). If we impose the same query complexity constraint on both algorithm, our local decoding algorithm yields smaller error probability when k = Ω(q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sup> ). In addition, our local decoding is efficient, i.e., the decoding complexity is Poly(k, q). Construction of an interleaved codex is based on concatenation of a codex with a multiplication friendly pair, while the main tool to realize codex is based on algebraic function fields (or more precisely, algebraic geometry codes).
Understanding group signature methods in making digital signatures to maintain the validity of messages

Abstract In this millennial era, a large amount of digital data traffic going through communication media on digital technology every day. Most of the data are documents and other essential information. The existing rapid development of technology nowadays, information can be easily faked. To make sure the validity of a digital document, a digital signature is required to verify the originality of the document. The purpose of this research is to design software which implemented the group signature algorithm to apply and verify digital signatures. The Signature Group algorithm used in this research is the Tseng-Jan scheme. The Tseng-Jan scheme consists of 5 stages: setup, join, sign, verify, and open. After applying the algorithm using 2 digits key and 3 digits key on the samples, 80% of the verification experiment on 2 digits key was succeeded 70% of the verification experiment on 3 digits key was succeeded.
High performance and safe flight of full‐scale helicopters from takeoff to landing with an ensemble of planners

Abstract Autonomous flight of unmanned full‐size rotor‐craft has the potential to enable many new applications. However, the dynamics of these aircraft, prevailing wind conditions, the need to operate over a variety of speeds and stringent safety requirements make it difficult to generate safe plans for these systems. Prior work has shown results for only parts of the problem. Here we present the first comprehensive approach to planning safe trajectories for autonomous helicopters from takeoff to landing. Our approach is based on two key insights. First, we compose an approximate solution by cascading various modules that can efficiently solve different relaxations of the planning problem. Our framework invokes a long‐term route optimizer, which feeds a receding‐horizon planner which in turn feeds a high‐fidelity safety executive. Secondly, to deal with the diverse planning scenarios that may arise, we hedge our bets with an ensemble of planners. We use a data‐driven approach that maps a planning context to a diverse list of planning algorithms that maximize the likelihood of success. Our approach was extensively evaluated in simulation and in real‐world flight tests on three different helicopter systems for duration of more than 109 autonomous hours and 590 pilot‐in‐the‐loop hours. We provide an in‐depth analysis and discuss the various tradeoffs of decoupling the problem, using approximations and leveraging statistical techniques. We summarize the insights with the hope that it generalizes to other platforms and applications.
The entropy source of pseudo random number generators: from low entropy to high entropy

The pseudo random number generators (PRNG) is one type of deterministic functions. The information entropy of the output sequences depends on the entropy of the input seeds. The output sequences can be predicted if attackers could know or control the input seeds of PRNGs. Against that, it is necessary that the input seeds is unpredictable, that is to say, the information entropy of the seeds is high enough. However, if there is no high enough entropy sources in environment, how to generate the seeds of PRNG? In other words, how to increase the entropy of the input seeds? Many approaches for extracting entropy from physical environment have been proposed, which lack of theoretical analysis. The condition of entropy's increasing is given. A model is built to verify the condition based on the functional programming language F*. An example of entropy's increasing is proposed utilizing execution time randomness of arbitrary codes. Then an algorithm is described, which can generate the seed when the entropy value is given.
Weight distribution of Hermitian codes and matrices rank

We are interested in calculating the weight distribution of Hermitian codes over the finite field with q2 elements. In this work we show that, if we can compute the partial weight distribution for about q2 weights, then we will easily compute the whole weight distribution (which has about q3 values). To achieve this, we actually show that the computation of a given element of the weight distribution is equivalent to a less laborious task, which is the classification of some submatrices of the parity-check matrix according to their rank.
Relation, Transition and Comparison Between the Adaptive Nearest Neighbor Rule and the Hypersphere Classifier

The Adaptive Nearest Neighbor (ANN) rule and the Hypersphere Classifier (HC) are two very simple and relatively new variants of the classical nearest neighbor (1NN) rule. Even if they share a similar formulation—they correct the query-to-prototype distance by taking into account the distance of the prototype to the nearest one from other classes—their relation has never been investigated. The main goal of this paper is studying this relation and providing an exhaustive performance comparison of both methods, highlighting occasions when their performances differ as well as identifying cases in which their application is advisable or leads to poorer results. Moreover, we propose a smooth transition between the two classifiers by studying the use of several convex combinations of their penalized distances. Experiments show that a combination is particularly helpful when both ANN and HC are worse than 1NN.
Scene flow estimation by depth map upsampling and layer assignment for camera-LiDAR system

This paper presents a scene flow estimation method which functions by depth map upsampling and layer assignment for the camera-LiDAR (Light Detection And Ranging) system. The 3D geometry and motion of the observed scene are estimated simultaneously based on two consecutive frames from a camera and a LiDAR. The proposed technique begins with dense depth map upsamling guided by a corresponding RGB image. The scene is then classified to various moving layers by a hybrid method. Finally, the motion of each layer is constrained by the RGB image and depth image which provide a coarse 3D rigid motion. Experimental results on both public datasets and a real-word platform demonstrate the effectiveness of this technique.
Recognition of horizontal gaze motion based on electrooculography using tsugeno fuzzy logic

Abstract Electrooculography is a biosignal activity generated from the human’s eyes activity. In this research, the eyes movements (left and right) were recognized by processing the EOG signal. The eyes movements in the horizontal movement were identified from the direction and the magnitude of the gaze angle. The direction of the eyes movements was detected using the polarity of the signal whereas the magnitude of gaze angle was computed using Tsugeno fuzzy method with two approaches, namely area of signal and peak of the signal. The system was able to detect the direction of gaze motion with the accuracy of 100%. The accuracy of the EOG signal in calculating the magnitude of the gaze angle was 93.46% for the right and 89.88% for the left. The number was higher compared to the performance of the peak point with the accuracy of 88.06% for the right and 84.79% for the left. The result in this paper showed that the system was able to detect the direction of eyes movements very well. However, the calculation of the magnitude of eyes movements is still a challenge to be improved in the future.